Spreadsheet (Use Data)
Final project instructions
This project requires that students take the initiative, make decisions, and question themselves, revising their ideas, adding to their ideas. Students are encouraged to consulting with others – reaching out to people that can help, as well as using Google, librarians, your other professors, this course’s professor, your colleagues sitting next to you.
Requirements:
You can use
more
than one
projects/worksheets/workbooks if you desire.
1. One Word document with a memo introducing and explaining your project (see requirements for that below).
a. See full requirements below.
b. Provide a clear, practical purpose and decision to be made using evidence provided by the outcome of the analysis, articulated in the memo and in the first documentation worksheet on the spreadsheet.
2. One spreadsheet with a particular name and structure (see below) containing the following work
a. Use at least TWO the following data analysis skills using the appropriate numbers: Count vs. sum vs. average vs. percent of column vs. percent of overall.
i. Compare categories – percent differences – A, vs. B, vs. C
ii. Look at trends – increasing, decreasing, consistency, variance
iii. Do an analysis using measures of central tendency: Mean or median. Compare the mean/median between different categories.
1. CALCULATE PROPORTIONS WHEN DOING COMPARISONS: (A-B)/Average(A,B)
iv. Do an analysis using measures of distribution: Discuss the shape of the distribution, the skew, as compared to a
normal distribution
. Use one of the following techniques:
1. You can also use a histogram or a column chart grouping values in a pivot table.
2. 5 number summary/box and whiskers chart.
v. Do an analysis with variance, perhaps comparing variance among various categories.
1. Option 1: Use 5 number summary (min, 1st quartile, median, 3rd quartile, max) or Box and Whiskers chart
2. Option 2: Look at the standard deviation. Relate the standard deviation to the mean (divide the SD by the Mean for the ratio, a high percentage means a lot of variance). Is there a lot of variance in relation to the mean or a little? What might that mean?
3. CALCULATE PROPORTIONS WHEN DOING COMPARISONS:
(A-B)/Average(A,B)
b.
You are required to use at least 20 skills. These skills are listed in worksheet in the DataForMidtermSp20.xlsx file, which also has a lot of data that might be used for analysis.
i. Provide a comment in a cell near where you carried out each skill with what skill was used and briefly why.
ii. You are required to do full formatting, whether they are skills in modules 5 or 8 or not.
iii. Do NOT include among the 20 skills anything covered in modules 1-4, not 5 and 8.
c. You must include at least one chart and one table, with full formatting as we learned throughout this semester. The chart you choose must be optimal for the purpose, i.e., you should read the document on how to choose a chart posted with the assignment.
d. You can use examples of other projects but you must copy NOTHING directly. If you want to use a template or an idea from an existing worksheet, you must re-create anything you want to emulate MANUALLY in your worksheet. The instructor has ways of figuring out if you cut/paste or if you use another worksheet other than that which you create. DON’T DO IT.
3. You will be evaluated not only on whether these skills are used, but the integrity of your project. That means your project and the skills you use must make practical sense. Skills used arbitrarily and randomly for no apparent reason will not receive credit.
There is a Canvas survey that is required that will bring you through the logical steps in creating this project.
Step by step instructions:
1. Excel file submitted
a. Create a new Excel file.
b. Save it as “LastNameFirstNameFinalProject”
c. In the first worksheet, put your name and explain the purpose of your spreadsheet – who might want to use it and for what purpose. This will be very similar to what you write in your second paragraph in your email/memo (see below). Then list the major elements of your solution – what the reader will see in the following worksheets. Walk them through it, so they understand what they are looking at and the purpose, step by step. Create the following table:
Worksheet
Table/Chart
Purpose
Example
Worksheet label A
Table “title of table 1”
Identify the highest rated x, y or z
“xxxx” is the highest rated
Table “title of table 2”
Compare the average of x, y or z
“xxxx” is the highest averages
Worksheet label B
Chart “title of chart”
Line chart showing trends in xxx
“xxx” is trending up recently
d. In the next and following worksheets, provide your solution.
e. Each worksheet should have a meaningful label.
f. Each worksheet should replicate that label in row 1, formatted in Title cell format.
2. Word file submitted.
a. Write the text of an email explaining what you have done to someone who might be interest. Note that you are attaching the spreadsheet for them to look at.
b. Three paragraphs:
i. In the first paragraph, provide the reasons for why you chose your project(s), and why someone else might be interested.
1. Explain the added value of your spreadsheet – what information does it provide that would not otherwise be had.
2. Exactly what decision does the reader want to make, and how is this analysis going to be worth looking at, i.e., what is the added value of your project? Why would they regret not having this information?
ii. In the second paragraph, broadly explain the basic logic of your analysis, process of your use of Excel.
1. Here, you are providing an
overview of all major objects and their purpose
. “This spreadsheet provides tools a, b, and c. Tool A provides the ability to analyze x…”
2. Then,
provide a step by step walk through with an example
. For example, “If you were interested in (a particular thing), then you would look at the table or chart in this particular way, providing that information.”
iii. In the final paragraph, note that you are willing to meet about this Excel project or answer any questions.
c. Use full professional formatting
i. Meaningful subject heading
ii. Salutation
iii. Closing
iv. Single spaced, block paragraphs (space between paragraphs, NOT INDENTED).
d. Use business style:
i. Proper grammar and spelling.
ii. You are not writing in street language to a friend, but formal language to a business colleague.
1. Use a thesaurus and a dictionary – do not use vague or imprecise terms.
iii. Neutral, 3rd person tone as much as possible – avoid first or second person language, e.g., “I”, and “you”.
iv. Logical hierarchy of writing:
1. First phrase of every sentence is main point; first sentence in the paragraph is the main point.
2. First sentence in every paragraph introduces and summarizes the entire paragraph.
v. Generally, use short, precise, clearly interrelated sentences.
vi. Do not submit a first draft – rewrite at least 3 times.
For example:
Poor writing: “I’ve noticed that you were looking for places to live that would best fit your salary. Please look at the attached file in this email that may help you.”
Problems:
· Contractions are too informal.
· Please re-write minimizing first and second person language. Business communication is neutral, 3rd person, impersonal as much as possible.
· Reflect on how specific and clear you are. What does “best fit for your salary” mean? What is a “fit” for a salary? Use a dictionary and thesaurus to find more specific terms.
Better:
“This email is written regarding your interest in choosing a place to live after graduation. To that end, I provide attached an Excel file that provides critical information for that decision. The Excel analysis considers two major concerns: The salary you can expect different areas and the cost of living in that area, in terms of housing and the cost of living index.”
Example of formatting: Obviously, you would not be sending a real email with an attachment! You are submitting a Word document in this (email) format.
Ideas on projects
1. Think of a way to use Excel, or multiple ways of using Excel.
a. Data will be posted to the assignment as well as a list of sources of data.
b. Google – ‘data analytics’, ‘data analysis’ for some ideas.
c. Go through the end of chapter review projects in the textbook.
d. Google your interests and see how people are thinking about these ideas.
e. Gather data from the internet about a topic you are curious about and answer a question. Examples: Data on movies, companies, celebrities, books, albums, works of art, artists, TV shows, cable channels, homes, cars, computers, phones, countries, states, cities, foods, wines, universities, professions, hospitals, airports, airplanes, restaurants…
f. Call people you know, or simply call a professional that might be familiar with your idea and ask them for guidance -most people are willing to help.
g. Ask people who run an organization or business and ask how you can help them solve a problem with Excel.
2. Think through what that might look like in Excel – what information you need, what form it would take in Excel, what functions or skills you might need to use.
3. Find the information you need, and experiment with the best way of utilizing that information, presenting that information in order to address your issue.
4. Consult with colleagues in this class.
5. Finally, CONSULT WITH THE INSTRUCTOR.
Final project
Final project
Data analysis (today’s lecture)
20 Excel skills (same as before)
Memo (same as before)
Appropriate numbers
Count, vs. sum, vs. average, vs. percent of…
Is this unfair?
There are four women top executives and 10 men
If there are only 4 women in the company?
If there are 92% women in the company?
What would be a more reasonable number? What else would you need to know?
Proportion: Percent of column total (number of execs/total number)
4 female executives out of a total of 4 female employees = %100
10 male executives out of 203 male employees = 5%
53 female and 67 male – leads to distortion using count
Appropriate charts
Picking the right type of chart (read the PDF provided in the assignment
Example: Pie chart is used to compare parts in relation to each other in terms of a whole. It is not effective for comparing two different things. In that case a column chart would be best.
Are women in this organization discriminated against?
This scale provides a more useful perspective
An alternative explanation to ‘discrimination: Are salary differences due to age, i.e., are women in this organization younger?
Are salary differences due to years of experience worked?
Are there systematic differences within level in the organization? Are differences in average salary due to more men at higher positions?
Do not mix separate analyses together, apples and oranges
Comparing data on different scales – examples:
Comparing GPA and working hours of male vs. female students in the same table
One possible solution: Use percent of column total
Poor use of chart: Comparing data with different scales
Poor use of chart: Comparing far too many variables
Female Montclair only Transfer Montclair only Transfer Montclair only Transfer Montclair only Transfer Freshman Junior Senior Sophmore 4 51 93 4 24 46 10 Male Montclair only Transfer Montclair only Transfer Montclair only Transfer Montclair only Transfer Freshman Junior Senior Sophmore 8 1 69 120 32 30 20 Other Montclair only Transfer Montclair only Transfer Montclair only Transfer Montclair only Transfer Freshman Junior Senior Sophmore 3
Review: Proportions
Average salary of men/women: $56,789 vs. $59,992
Difference $3,201
M/F – What is M in relation to F?
“M average salary is 95% of F”
F/M – What is F in relation to M?
“F average salary is 106% of Male” or “..6% more than male”
Percent difference (M-F)/Average(M,F) is 6%
Measures of central tendency
Mean (average)
Median (pick middle number)
Better if there are outliers, extreme numbers, skew in the data
Average salary of those in this room gets skewed if Bezos walks in… pick the median to get a better measure of the group
Example – Income in a group can vary widely, so median is frequently used
Why do we care about mean/median?
What are some common ways we think about the world using that statistic?
GPA, Earned Run Average, Batting Average
Provides information about a set or group – how they tend as a whole
What can you do with it?
The difference in GPA (numeric variable) by category
Male/Female (categorical variable
Hours worked: Average GPA by hours worked
Chunking a numeric variable to use it as a categorical
Chunks of hours worked: 0-9, 10-19, 20-29, 30-39, 40+
Pivot table – use the Grouping function on the ribbon
Alternative: Scatterplot
Measures of variance
Range, min, max
Standard deviation (from the mean)
How scattered the data is around the mean
Higher numbers mean more variance
Whether variance is high or low: SD/Mean
Higher percent means higher variance
Why do we care about variance?
Compare categories by variance
Men have more variance, and greater range of salaries – what does that mean?
Distribution
How data falls
Normal distribution as benchmark: Natural distribution given no bias, i.e., something skewing the data
Analysis of distribution: Box and Whiskers chart, 5 number summary: Min, 1st quartile, median, 3rd quartile, Max
PEWRESEARCH.ORG
Excellent source of data analysis examples, tables, charts
Excellent example of how to write with and about data analysis
Source of data: I have already provided some of their data for you to analyze yourself.
Question: Did Mayor Giuliani’s policies lead to decline in crime in NYC?
Question: Who are our students and how can we help them? What should we focus on? Are there many students living in poverty? How many students are living in poverty? Is that increasing or decreasing? Is that related to whether they are still with their parents?
Young college grads are complaining about their financial situation – is it true that they have a greater debt? What is that tend like? Does it look like there is a problem that needs to be addressed?
Has anything changed with immigration given the recent news of families being separated? Or is that simply a Trump policy?
Salary
demanded
26,041$
26,459$
26,900$
27,116$
27,382$
27,736$
27,999$
28,670$
28,961$
29,089$
30,647$
30,863$
31,695$
31,903$
32,906$
34,084$
34,232$
34,508$
34,619$
35,019$
To: Salvador Apari, Director of Maintenance, Montclair State University Comment by William Colucci: Standard memo heading. You are NOT writing to the instructor. You are writing to a decision maker who has requested that you gather and analyze data to inform a decision.
From: Luke Skywalker, Institutional Research senior analyst
CC: Susan Cole, President, Montclair State University
Subject: Evidence of gender bias? Analysis of salary/gender data
Date: April 30, 2019
During our April 26th meeting, it was determined that an analysis of salary differences by gender in our department was needed to determine if there is any evidence of gender bias. The concern is raised by the opinion of some supervisors that women feel they are unfairly treated. This might mean that the department will not be able to retain or attract quality female talent, and that the University could be at risk of lawsuits for discrimination. Comment by William Colucci [2]: Why are we doing the analysis? Typically, in business, the analyst will be briefed on the decision in question and asked to gather/analyze relevant data to help inform the decision.
The data set contains salary, gender, level, and length of employment gathered from HR records in this past month. This includes all employees, 67 men and 53 women. Since the data includes all employees, is recent and derived from reliable HR records, it is relevant and valid. Comment by William Colucci [2]: Describe the data in detail – what data was gathered, when, and how? Why is it reliable and valid (relevant)?
In order to evaluate the data, we discussed ways of evaluating any differences in salary that might be found. In looking at data available on departments at MSU, or comparable universities, as well as comparable industry wage gaps, it was determined that a wage gap of 8% was common. It was determined that anything above that would be cause for concern, and a gap greater than 25% of that (10% wage gap) would lead to investigation. Men and women should have similar salary variance, which indicates a wider or lesser range of opportunities in the organization. Research on the wage gap issue suggests that explanations other than gender bias include differences in choices men and women make in terms of jobs, number of hours worked, length of employment. Men and women may tend to make different life choices which impact overall career success. Thus, differences between men and women in terms of level and number of years of work experience were looked at. Comment by William Colucci [2]: Your case study provides you with information about what empirical benchmarks, i.e., how to evaluate empirical findings (the numbers, differences in average, standard deviation, as well as patterns in distribution. This is done through investigation of comparisons that can be used to judge the meaning of outcomes, i.e., how the results will be interpreted in terms of making the decision.
The first analysis was to determine whether there was an overall wage gap. Table 1 (reported in the appendix) reports the average salary by gender. Men on average earn $5785 more than women. Thus, on average, women earn 90% of men. The percent difference is 11%, which, given the benchmarks discussed above, warrants the investigation reported below. Comment by William Colucci [2]: Compare measures of central tendency (average) – what is the raw figure, what is the proportion? Speculate on what this means in terms of the benchmarks discussed above and in real world terms.
Variance or the range of salaries was examined, as this might provide insight into the differences in average salary. Table 1 data (standard deviation, reported in the appendix) reports that women have a lower minimum, and men a greater maximum salary. Men have a greater range of salaries, an 18% difference. The variance in salaries is greater for men, a 9% difference. Men clearly have access to a greater range of positions in the company, especially towards the higher salary levels. Comment by William Colucci [2]: Compare measures of variance (standard deviation) – what is the raw difference? What is the proportion of the difference, i.e., the percent difference? Discuss what this means in terms of the benchmarks laid out in the case study. Exactly how does this inform the reader on how to make his or her decision? Use practical language: What should the decision maker think, feel, do?
Chart 1 in the appendix addresses an explanation for the wage gap in terms of levels of employment, i.e., that males might have a higher salary because they are more often employed at higher paying levels in the organization. It is clear that women dominate at lower ends of the hierarchical ladder, and males towards the higher end, suggesting that the gap might be due simply to which gender holds which position. Chart 2 looks directly at average salaries within levels, and shows very similar salary levels. It also shows that at the highest level there are no female employees. This also might provide an explanation for the wage gap, i.e., that the highest paid positions are held only by men. Comment by William Colucci [2]: Introduce charts or tables before the reader sees them, or reference where they are located. Explain to the reader what data the figure summarizes and how to read the chart/table. Discuss distribution and how that might inform the decision at hand. Reference the benchmarks provided in the case study description. Make any other comments about insights you can provide or speculations about the meaning of the data for the practical question at hand. Tell the reader exactly what to think, feel or do in practical terms.
While there is a substantial pay gap in raw average salary, and in terms of the benchmarks and criterion agreed upon, yet additional analysis suggests this gap is not due to a gender bias as much as simply greater male presence in higher levels in the organization. While that fact in turn might be highlighted as evidence of bias, this analysis does not provide any evidence on that matter. It seems clear there is clear empirical evidence to explain differences in average salary in terms other than mere gender prejudice/bias in this case. Comment by William Colucci [2]: Conclusion – review the basic findings; suggest exactly what the reader should think, fell, do.
Please do not hesitate to contact me with any comments or questions regarding the above information and analysis. I would also be happy to conduct additional research upon your request. Comment by William Colucci: Closing making yourself available for questions and suggesting further analyses. What other variables or factors might provide further insight?
Appendix
Table 1
Chart 1
Chart 2
Page 2 of 2
Example descriptive statistics analysis, step by step
This document is just a walk-through example of how data analysis might be done and why. It is NOT directing students to do all of this. It is simply provided to provide a better idea what data analytics is all about, and an example to help you figure out your project.
1. Note what data is on hand. In this case:
a. Income in terms of billions of dollars from last year, 2018. This income is from the accounting department and considered accurate and the latest information available.
2. Note what decisions were made on how to evaluate outcomes, i.e., exactly what numbers will lead to what decision?
a. The manager of Exxon has decided 3% greater average sales in Asian market compared to the European market will mean that he will build a new refinery in Asia.
b. The manager is also concerned with how regular the income will be as well as how much more income. If the variance in income from Asia is much high than Europe, he will NOT build a refinery in Asia, as that income will be unreliable. He has defined excessive variance at 10% difference.
c. The manager is also concerned with how regular the income will be as well as how much more income. If the variance in income from Asia is much high than Europe, he will NOT build a refinery in Asia, as that income will be unreliable. He has defined excessive variance at 10% difference.
3. First look at the difference between measures of central tendency/average:
a. Example:
i. The case study reads thus: The manager of Exxon has decided 3% greater average sales in Asian market compared to the European market will mean that he will build a new refinery in Asia.
ii. Look at the average data to find out what the outcomes are.
1. The Asian sales are 90 billion dollars and the European sales are 80 billion dollars. The difference is divided by the average of both – 10/85 or 12%.
iii. You advise the client to build the refinery in Asia.
4. Second look at the differences between measures of variance/standard deviation:
a. Example:
i. The case study reads thus: The manager is also concerned with how regular the income will be as well as how much more income. If the variance in income from Asia is much high than Europe, he will NOT build a refinery in Asia, as that income will be unreliable. He has defined excessive variance at 10% difference.
ii. Look at the standard deviation data to find out what the outcomes are:
1. The Asian sales standard deviation is 8%, and the European sales standard deviation is 7%. The difference is 1/7.5 or 17% difference.
iii. You advise the client not to build the refinery despite the greater Asian sales because he has stated that he will not go forward with the project if the variance is over 10%.
5. Third look at the distribution, the chart.
a. Example:
i. The case reads thus: The manager wants to know why there is variance over the course of the last year and wants you to track income by quarter.
ii. You analyze a chart of profits by quarter and find that the Asian profits decline in the beginning and end of the year and are very high in the summer and fall.
iii. You advise the client that there is something the reduces profit in Asia in the spring and winter that should be investigated.
6. Conclusion
a. The difference in income between the regions is substantial and suggests building facilities in Asia. Yet, the difference in variance shows that will be risky. Therefore, management should not go forward with building the facilities in Asia unless a different view on how much risk is acceptable is chosen. The facility should not be build. Further investigation into the reasons for the seasonal differences might lead to a different decision, or at least better understanding.
7. The reader is invited to ask further questions.
Profit: European vs. Asian, 2018 (billions)
EuropeanAsian
Average8090
Standard Deviation78
Variable INFO
A random sample of 66 NYC restaurants rated by Zagat with their characteristics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Source: | Zagat NYC | Restaurant | 0 | 1 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Variables | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Name of NYC Restaurant | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cost | Cost rating (in $) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Food | Food rating on scale from 1 (poor) to 30 (outstanding) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Décor&Service | Combined Décor and Service ratings, each on 1 to 30 scale, so this rating is from 2 (poor) to | 60 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bang for Buck | An index expressing dollar value for the food | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Popularity Points | An index measuring restaurant popularity | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Type Food | 1= | Asian | 0= | No | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cuisines | (American, | French | Italian | Mexican | Chinese | Indian | Japanese | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
HighPopIndx | Whether the Zagat Popularity Index is | 90 | Yes |
Zagat DATA
DecorService | TypeFoodCODE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Gotham Bar & Grill | 81 | 28 | 52 | 1 | 7 | 23 | 38 | Non-Asian | American (New) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mas (Farmhouse) | 88 | 27 | 51 | 15 | 3 | 74 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Union Square Cafe | 70 | 49 | 18 | 29 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Four Seasons Restaurant | 10 | 55 | 14 | 1 | 42 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Saul | 69 | 45 | 2 | 20 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Gramercy Tavern | 11 | 53 | 12 | 33 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Blue Hill | 83 | 50 | 16 | 104 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Craft | 82 | 26 | 5 | 24 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Perry St. | 19 | 2 | 96 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Dovetail | 48 | 755 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Shun Lee Palace | 59 | 25 | 3 | 47 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mr. K’s | 152 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Shun Lee West | 56 | 317 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Chin Chin | 54 | 21 | 194 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mr. Chow | 76 | 22 | 1 | 44 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Tang Pavilion | 41 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Shun Lee Cafe | 46 | 37 | 196 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Pig Heaven | 39 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
La Caridad | 78 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Daniel | 13 | 26 | 80 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Le Bernardin | 146 | 3556 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bouley | 20 | 43 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Eleven Madison Park | 117 | 2917 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Jean Georges | 127 | 22 | 89 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
La Grenouille | 114 | 1203 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Café Boulud | 1 | 58 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Jack’s Luxury Oyster Bar | 71 | 86 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Tocqueville | 339 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Triomphe | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Tamarind | 5 | 84 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Amma | 40 | 222 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Yuva | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Dawat | 131 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Junoon | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Salaam Bombay | 36 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mint | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Darbar | 34 | 63 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Tulsi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Darbar Grill | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Marea | 101 | 12 | 85 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Del Posto | 102 | 2050 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Ai Fiori | 93 | 435 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Scalini Fedeli | 377 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Il Buco | 64 | 328 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Il Buco Alimentari e Vineria | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Piccola Venezia | 207 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sistina | 116 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Il Tinello | 199 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
San Pietro | 87 | 121 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sushi Yasuda | 651 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kuruma Zushi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sushi of Gari 46 | 538 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Zenkichi | 61 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Nobu | 1521 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Nobu, Next Door | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sushiden | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Megu | 217 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Super Tacos | 100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Toloache | 265 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Maya | 175 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Oaxaca | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Dos Caminos | 529 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Alma |
Scatter
Plot
>Scatter Plot
of based on (in Lbs) of Vehicle MPG 5
5
5
05
5
5
5
85
5
85
05 50 3 0 00
5
0
0
80
20
0
3 5 0
15
30
20
0
50
5
3 5 00
40
5
5
05
3480 0
5
5
0
5
3880 5 5 0
5
0
25
4 0 50
0
5
5
0
0
0
3480 3345 4 5 3430 5
0
3 5 80 0
5
5
3 0 3540 3545 3650 5
4345 4490 4550 3555 17 18 18 23 25 21 17 15 20 24 19 18 19 17 26 15 18 19 17 18 17 27 23 21 18 23 20 14 15 18 16 16 20 23 17 22 17 12 16 19 16 15 16 19 13 15 20 20 16 31 21 21 34 19 17 25 14 28 19 27 18 23 18 17 19 19 19 19 19 13 22 14 20 15 18 23 28 21 17 15 28 18 13 23 20 15 24 21 19 21 16 20 13 17 29 18 16 27 18 30 23 23 18 21 15 16 17 17 21 16 23 30 23 17 23 19 20 25 13 21 19 15 18 26 29 17 22 20 23 27 20 22 23 21 20 16 24 17 26 23 22 18 21 16 27 18 22 17 16 22 24 21 29 17 18 44 23 18 34 25 24 24 23 22 20 20 16 X Y 5
5 17 19 17 18 0 17 0
23 23 20 5
18 16 20 17 22 16 16 19 13 15 21 19 17 25 5
27 18 19 3890 19 45
13 14 21 17 15 5 28 18 5
13 23 20 15 16 13 27 16 23 3345 23 17 3430 23 19 20 25 29 27 24 23 22 3545 21 27 22 4550 18 25 20 Data
4595 17 21 0
26 17 22 16 13 13 15 17 25 23 SLR Analysis
86
, Standard Error
4
757
, .0000
6
, Residual SS
1.1804
1
F .1804 170 696
Lower 95% Upper 95% 39.0902 0.9786 457
0.0000 4
37.1584 7
814
0.0000 -0.0054 3
4505 4085 3480 4420 3880 3890 3870 3480 4235 3345 3430 5715 4550 3305 3335 3915 3540 3650 4190 3280 4490 3485 3465 3555 25 8
3800 3480 2000 6325 652
4 171 21 16 19 20 18 16 19 20 19 16 18 20 18 23 23 16 21 17 17 20 2 Gps
MPG by of Vehicle 9%
65 DataCopy CompleteStats 7
761
3%
Count 171 Minimum 12 17 Median 19 23 Maximum 44 MPG MPG 12 1 Five-Number Summary Minimum 13 12 18 Maximum 44 27 MPG based on Origin of Vehicle Asia or Europe US 13 1 12 2.5 Median 3800 Maximum 6325 Weight Weight (lb) 2000 1 Asia or Europe Origin of Vehicle 171 Auto Type by Origin of Vehicle Asia or Europe US 6 1 Asia or Europe US 33 27 Asia or Europe US 17 6 Asia or Europe US 4 3 Asia or Europe US 40 25 Asia or Europe US 6 3 Weight Weight (lb) HP Origin Type Auto Transmission 17 66 4595 0
36 300 8 40 Asia or Europe 4 Door SUV 18 74 65 4015 870 30 41 Asia or Europe 4 Door SUV Auto 5 18 73 57 4085 850 13 300 43 Asia or Europe Sedan Auto 5 23 195 74 57 3705 850 13 6.7 42 Asia or Europe Sedan Auto 5 25 169 56 3305 20 200 35 Asia or Europe 4 Door Hatchback 21 194 71 57 112 4115 16 40 Asia or Europe Sedan 17 75 57 121 4505 18 330 41 Asia or Europe Sedan Auto 6 15 200 78 68 5465 5
37 350 146 40 Asia or Europe 4 Door SUV Auto 6 20 181 72 56 104 3920 5
13 340 140 38 Asia or Europe Sedan 24 72 56 109 3485 11 215 7.4 37 Asia or Europe Wagon Auto 6 22 191 73 58 114 3785 1100 14 300 149 39 Asia or Europe Sedan Auto 6 19 190 73 54 109 3885 840 13 360 40 Asia or Europe Coupe Auto 6 18 204 75 59 4505 1060 18 325 6.9 42 Asia or Europe Sedan Auto 6 19 180 73 66 110 4065 33 260 140 39 Asia or Europe 4 Door SUV Auto 6 17 191 76 70 116 5025 0
36 260 142 42 Asia or Europe 4 Door SUV Auto 6 26 161 70 50 98 3170 550 2 6 33 Asia or Europe Coupe Man 6 15 78 70 5100 44 43 US 4 Door SUV Auto 6 18 198 73 57 3565 915 16 200 9 40 US Sedan 19 74 58 116 4095 925 17 44 US Sedan Auto 4 17 75 58 116 4085 19 275 7.6 180 46 US Sedan Auto 4 13 203 79 75 116 5810 47 181 42 US 4 Door SUV Auto 6 16 195 73 68 116 4730 37 255 42 US 4 Door SUV Auto 5 18 73 58 116 4000 890 14 255 7.1 146 40 US Sedan Auto 5 17 178 72 50 106 3680 400 4 320 145 41 US Coupe Auto 5 27 154 66 59 98 2560 860 7 146 35 US Sedan 23 180 68 57 103 2920 890 14 145 152 37 US Sedan Auto 4 21 175 73 49 106 3280 11 400 5 145 42 US Coupe Man 6 18 71 69 3880 37 185 43 US 4 Door SUV Auto 5 23 69 65 104 3265 30 9.2 41 US 4 Door SUV Auto 4 20 200 73 59 111 3710 945 19 155 41 US Sedan Auto 4 14 79 77 5935 61 320 9.1 45 US 4 Door SUV Auto 4 14 79 77 116 5715 51 320 9 42 US 4 Door SUV Auto 4 15 75 75 113 4830 39 9.1 159 39 US 4 Door SUV Auto 4 18 72 72 121 4420 0
51 240 8.8 153 43 US Minivan Auto 4 16 197 74 58 120 4105 865 16 340 6.4 139 41 US Sedan Auto 5 16 79 67 116 4720 36 8.6 146 42 US 4 Door SUV Auto 6 20 169 67 61 103 3350 865 32 180 165 41 US Sedan Auto 4 23 191 73 59 109 3335 825 13 40 US Sedan Auto 4 17 203 77 69 121 4755 62 251 8.8 172 42 US Minivan Auto 6 22 69 60 104 3185 865 20 172 39 US 4 Door Hatchback CVT 17 200 75 58 120 3895 865 16 340 6 41 US Sedan Auto 5 12 201 76 74 119 5150 45 335 7.6 43 US 4 Door SUV Auto 5 16 203 77 69 121 4600 1150 62 197 10.3 42 US Minivan Auto 6 19 198 74 58 120 3950 865 30 8.8 41 US Wagon Auto 4 16 73 69 109 4340 1150 40 210 9.1 167 39 US 4 Door SUV Auto 4 15 176 75 48 99 3480 465 6 510 130 43 US Coupe Man 6 16 76 67 111 4540 36 265 8.3 159 39 US 4 Door SUV Auto 6 19 175 70 70 103 3605 1000 38 200 215 40 US 4 Door SUV Auto 4 13 221 79 77 6325 73 300 9.1 166 45 US 4 Door SUV Auto 6 15 74 73 114 4905 5
48 210 9.7 38 US 4 Door SUV Auto 5 20 190 72 56 3480 850 16 221 8 42 US Sedan Auto 6 20 74 55 107 3585 720 13 300 144 39 US Coupe Man 5 16 200 75 67 113 4420 1150 38 152 42 US Sedan Auto 6 31 69 57 106 2810 850 13 140 8.6 39 Asia or Europe Sedan Man 5 21 178 72 66 103 3505 850 26 166 139 39 Asia or Europe 4 Door SUV Auto 5 21 170 72 70 3650 47 166 36 Asia or Europe 4 Door SUV Auto 5 34 162 67 60 98 2370 850 24 109 9.9 144 36 Asia or Europe 4 Door Hatchback Man 5 19 201 77 69 118 4615 67 255 8.6 144 40 Asia or Europe Minivan Auto 5 17 191 79 73 109 4510 1320 48 255 8.2 157 40 Asia or Europe 4 Door SUV Auto 5 25 162 69 51 95 2855 400 5 240 5.8 134 36 Asia or Europe Coupe Man 6 14 75 74 112 4940 940 38 220 170 37 US 4 Door SUV Auto 4 28 169 67 58 98 2590 850 12 110 153 36 Asia or Europe Sedan Auto 4 19 193 73 59 109 3835 860 17 263 7.1 144 41 Asia or Europe Sedan Auto 5 27 177 70 58 104 2945 850 14 138 10.4 152 37 Asia or Europe Sedan Auto 4 18 74 68 106 4345 38 242 8.5 139 39 Asia or Europe 4 Door SUV Auto 5 23 189 72 58 107 3345 860 16 162 10.5 133 39 Asia or Europe Sedan Auto 4 18 170 71 66 104 3800 860 31 173 143 39 Asia or Europe 4 Door SUV Auto 4 17 191 77 69 110 4610 42 260 8.6 154 41 Asia or Europe 4 Door SUV Auto 6 19 187 70 57 112 3635 14 306 132 38 Asia or Europe Sedan Auto 5 19 193 71 59 114 3890 860 13 275 6.9 141 39 Asia or Europe Sedan Auto 5 19 192 72 56 115 3880 905 14 294 142 40 Asia or Europe Sedan Auto 6 19 200 73 57 119 3860 880 17 294 7.1 143 41 Asia or Europe Sedan Auto 6 19 189 82 52 108 3890 705 10 300 7 138 38 Asia or Europe Coupe Auto 6 13 189 75 72 110 5245 1100 39 330 7.3 160 41 US 4 Door SUV Auto 5 22 173 69 65 104 3380 925 27 172 10.1 163 38 US 4 Door SUV CVT 14 186 73 67 109 4690 33 235 8.8 159 40 US 4 Door SUV Auto 5 20 174 69 64 104 3575 925 30 172 168 38 US 4 Door SUV CVT 15 173 74 71 116 4550 850 35 205 172 43 US 4 Door SUV Auto 4 18 197 73 59 110 3870 860 16 200 9.1 160 41 Asia or Europe Sedan Auto 5 23 186 71 58 107 3430 825 15 162 9.2 142 39 Asia or Europe Sedan Auto 5 28 167 67 58 98 2615 850 9 110 150 35 Asia or Europe Sedan Auto 4 21 179 72 65 106 3660 825 33 182 163 39 Asia or Europe Wagon Auto 5 17 202 78 69 119 4725 65 9 152 43 Asia or Europe Minivan Auto 5 15 181 73 68 107 4310 880 32 192 9.5 155 39 Asia or Europe 4 Door SUV Auto 4 28 176 68 58 103 2875 850 12 138 9.5 153 37 Asia or Europe Sedan Man 5 18 170 71 66 104 3750 860 31 173 172 38 Asia or Europe 4 Door SUV Auto 4 13 191 75 74 114 5705 5
52 300 9.1 145 39 US 4 Door SUV Auto 6 23 191 72 57 109 3670 900 15 6.4 140 39 Asia or Europe Sedan Auto 6 20 190 72 57 112 3915 13 245 7.4 147 38 Asia or Europe Sedan Auto 6 15 188 74 73 110 4825 5
40 235 8 136 41 Asia or Europe 4 Door SUV Auto 5 24 180 71 56 108 3510 825 13 204 7.7 134 35 Asia or Europe Sedan Auto 6 21 203 74 58 122 4515 825 18 380 153 40 Asia or Europe Sedan 19 186 73 66 107 4235 925 33 270 7.3 141 40 Asia or Europe 4 Door SUV Auto 5 21 179 72 53 103 3870 8 144 38 Asia or Europe Coupe Auto 6 16 187 76 67 111 4620 910 37 265 8.2 162 41 US 4 Door SUV Auto 6 20 191 72 57 107 3625 875 16 263 6.9 135 42 US Sedan Auto 6 13 208 79 78 119 6245 57 300 8.8 167 42 US 4 Door SUV Auto 6 17 215 78 59 118 4415 1100 21 154 42 US Sedan Auto 4 29 149 68 44 91 2000 550 4 190 4.6 132 37 Asia or Europe Coupe Man 6 18 184 74 65 108 4025 850 28 244 9.1 140 41 Asia or Europe 4 Door SUV Auto 6 16 200 76 68 113 4585 38 263 3.2 161 40 Asia or Europe 4 Door SUV Auto 6 27 157 68 49 92 2510 340 5 170 6.7 138 33 Asia or Europe Coupe Man 6 18 174 70 53 106 3085 680 8 6.7 130 38 Asia or Europe Coupe Man 6 30 178 69 58 104 2830 850 11 148 8.6 140 36 Asia or Europe Sedan Man 5 23 182 69 64 108 3480 1020 39 157 10.3 154 37 Asia or Europe Wagon Auto 4 23 187 70 57 105 3355 850 15 160 9.6 145 41 Asia or Europe Sedan Auto 4 18 194 74 55 112 4060 915 16 133 38 Asia or Europe Sedan 21 190 72 58 112 3845 16 268 6.5 152 38 Asia or Europe Sedan Auto 7 15 200 76 72 121 5575 46 335 7.4 154 42 Asia or Europe 4 Door SUV Auto 7 16 189 75 70 115 4845 41 268 7.8 143 38 Asia or Europe 4 Door SUV Auto 7 17 205 73 58 4490 16 6 154 40 Asia or Europe Sedan Auto 7 17 179 72 51 101 4235 570 10 382 5.3 132 36 Asia or Europe Coupe Auto 7 21 161 70 51 96 3315 525 10 268 6.2 130 34 Asia or Europe Coupe Man 6 16 78 57 115 4180 1100 21 239 8 42 US Sedan Auto 4 23 191 72 56 107 3320 850 16 160 9.5 144 40 US Sedan Auto 5 30 146 66 55 97 2690 815 6 172 154 37 Asia or Europe Coupe Man 6 23 180 72 54 101 3345 660 16 162 149 42 Asia or Europe Coupe Man 5 17 190 74 70 108 4195 40 215 8.2 147 41 Asia or Europe 4 Door SUV Auto 4 23 190 72 58 108 3430 825 13 160 9.1 148 43 Asia or Europe Sedan Auto 4 19 71 66 105 3925 1155 34 220 8.3 142 38 Asia or Europe 4 Door SUV Auto 6 20 169 72 52 104 3640 450 4 287 6.2 130 37 Asia or Europe Coupe Man 6 25 190 71 58 109 3215 900 16 175 8.1 142 39 Asia or Europe Sedan CVT 13 208 79 76 123 5715 59 317 7.2 140 44 Asia or Europe 4 Door SUV Auto 5 21 191 73 58 109 3555 900 14 255 150 44 Asia or Europe Sedan CVT 19 189 74 67 111 4190 900 31 245 8 146 40 Asia or Europe 4 Door SUV CVT 15 188 73 70 112 4875 48 270 8 172 43 Asia or Europe 4 Door SUV Auto 5 18 204 78 70 4550 60 235 153 45 Asia or Europe Minivan Auto 5 26 180 71 60 106 3010 850 13 140 9.6 38 Asia or Europe Sedan CVT 29 169 67 60 102 2780 860 18 122 9.5 187 37 Asia or Europe Sedan Man 6 17 179 73 75 106 4480 920 46 261 7.7 158 40 Asia or Europe 4 Door SUV Auto 5 22 189 71 57 112 3540 890 14 169 156 42 US Sedan Auto 4 20 198 72 56 111 3630 915 16 200 8.3 169 41 US Sedan Auto 4 23 157 71 50 95 2925 355 4 177 7.2 148 36 US Coupe Man 5 27 173 70 61 102 3000 850 24 126 10.7 150 38 US Wagon Auto 4 20 176 71 52 93 3305 660 5 355 127 37 Asia or Europe Coupe Man 6 22 172 70 51 95 3015 420 9 240 6.5 123 37 Asia or Europe Coupe Man 5 23 182 68 57 105 3370 15 210 8 136 37 US Sedan Auto 5 21 190 71 57 106 3545 930 16 260 7.2 139 40 US Sedan Auto 5 20 190 70 58 112 3335 915 16 8.1 160 42 US Sedan Auto 4 16 201 78 70 119 5015 1320 49 275 8.8 162 42 US 4 Door SUV Auto 6 24 161 71 50 95 3065 4 260 6.2 156 37 US Coupe Man 5 17 180 73 67 107 3915 915 28 8.2 162 42 US 4 Door SUV Auto 6 26 174 69 56 106 2890 865 13 160 8.8 147 37 Asia or Europe Coupe Man 5 23 167 69 64 102 3120 850 34 158 9.4 148 37 Asia or Europe Wagon Auto 4 22 177 68 65 99 3260 900 32 173 10 142 38 Asia or Europe 4 Door SUV Auto 4 18 186 68 56 105 3540 850 11 250 151 38 Asia or Europe Sedan Auto 5 21 189 70 63 105 3545 900 31 168 158 38 Asia or Europe Wagon Auto 4 16 192 74 66 108 4280 1155 36 256 8.6 162 40 Asia or Europe 4 Door SUV Auto 5 27 177 68 57 102 2840 875 12 126 9.5 157 36 Asia or Europe 4 Door Hatchback Man 5 18 177 71 67 104 3650 905 25 185 9.5 147 38 Asia or Europe 4 Door SUV Auto 5 22 163 69 63 98 2995 815 16 143 150 37 Asia or Europe 4 Door Hatchback Auto 4 17 197 72 69 112 4190 1190 37 7.7 147 44 Asia or Europe 4 Door SUV Auto 5 16 189 74 72 110 4345 45 239 8.2 146 40 Asia or Europe 4 Door SUV Auto 4 22 197 73 59 111 3600 875 14 280 6.7 155 41 Asia or Europe Sedan Auto 5 24 189 72 58 109 3280 900 15 158 9.6 151 38 Asia or Europe Sedan Auto 5 21 193 72 56 107 3620 755 14 225 7.5 138 39 Asia or Europe Coupe Auto 5 29 178 67 58 102 2595 860 14 130 151 38 Asia or Europe Sedan Auto 4 17 184 75 72 106 4350 1190 34 239 8 157 44 Asia or Europe 4 Door SUV Auto 5 18 188 75 69 110 4490 38 270 8 151 42 Asia or Europe 4 Door SUV Auto 5 44 175 68 58 106 2950 825 16 110 10.5 152 37 Asia or Europe 4 Door Hatchback CVT 23 181 72 66 105 3485 825 39 166 9.8 139 37 Asia or Europe 4 Door SUV Auto 4 18 201 77 69 119 4550 1050 71 7.7 147 39 Asia or Europe Minivan Auto 5 34 151 67 60 97 2410 845 9 106 9.3 186 34 Asia or Europe Sedan Man 5 25 166 69 58 102 3155 15 200 6.7 141 36 Asia or Europe 4 Door Hatchback Man 6 24 179 69 58 102 3330 970 16 150 9.4 146 36 Asia or Europe Sedan Auto 6 24 188 72 58 107 3465 14 200 7.7 145 38 Asia or Europe Wagon Auto 6 23 176 70 57 104 3245 950 14 168 9.4 148 37 US Sedan Auto 5 22 180 71 56 107 3465 890 14 208 7.9 145 42 US Sedan Auto 5 20 191 73 59 112 3850 905 15 235 8.6 158 39 US Sedan Auto 6 20 178 70 57 104 3555 800 28 8.5 153 36 US Wagon Auto 5 16 189 75 70 113 4950 1210 38 7.6 138 42 US 4 Door SUV Auto 6 >StudentSurveyVariableList
2 cases
through Spring 1 , from Prof. William Colucci and Daphne Hanrahan’s BUGN2 courses
Level
na na nominal na nominal na nominal -credit classes are you taking this semester?
na ordinal , , etc.
na ordinal na nominal On a scale of 1 to 7, 1 is uncertain and 7 is very enthousiatic/passionate. nominal Grade
na na numeric na numeric or ?
na numeric 0 (or equivelent for transfer students)?
numeric 1 is not at all confident and 7 is extremely confident. numeric 1 is not at all confident and 7 is extremely confident. numeric Presidential campaign?
nominal na nominal na nominal na nominal na numeric Dorm
na na nominal 3: What is your class designation?
9: Do you commute or live in a dorm?
: How strongly do you feel about your choice of major? Â On a scale of 1 to 7, 1 is uncertain and 7 is very enthousiatic/passionate.
(or equivelent for transfer students)? Â 1 is not at all confident and 7 is extremely confident.
: Gender Transfer CommuteDorm Major HSAverage Age ElectionInterest _ SP16
3 1 Transfer 0 20 4 6 Mac 3 5 3 6 0_22SP16
Junior 3 0 1 Full Time 4 1 21 Mac 3 4 1 4 22 Mac 5 6 1 Yes 3 PC 4 2 2 No 4 4 5 21 Mac 4 5 3 No 5 PC 1 7 7 Yes 4 2 Male 1 Transfer 0 Full Time 30 4 6 19 PC 4 6 4 No 5 4 Male 1 Transfer 0 Full Time 45 3 7 2.8 Mac 4 5 5 Yes 1 45 3 6 5
Mac 6 6 6 Yes 5 22: :45 UTC
Senior 4 Female 0 Transfer 0 Commute 0 Full Time 0 6 6 Mac 5 4 7 6 7 Senior 4 Female 0 Transfer 0 Commute 0 Full Time 6 7 3.4 22 Mac 5 6 5 No 3 Senior 4 Female 0 Transfer 0 1 Full Time 40 6 Marketing 6 2.9 85 22 Mac 5 3 4 7 Instagram 7 Junior 3 Male 1 Montclair only 1 Live in dorm 1 Full Time 0 6 2 75 20 PC 3 3 7 10 Facebook No 4 SP16
UTC
Junior 3 Female 0 Transfer 0 Commute 0 Full Time 16 6 6 90 21 Mac 7 7 7 20 Instagram Yes 5 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 40 6 Other 7 3.3 80 26 Mac 4 5 3 10 Instagram No 1 Junior 3 Female 0 Montclair only 1 Live in dorm 1 Full Time 6 Marketing 4 3.4 20 Mac 4 6 6 18 Instagram No 4 : UTC
Junior 3 Female 0 Transfer 0 Live in dorm 1 Full Time 20 6 Management 7 80 21 PC 3 4 5 10 Instagram No 1 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 13 6 7 7
90 22 PC 5 7 5 20 No 6 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 30 6 Finance 6 80 22 6 5 5 15 Yes 5 Junior 3 Male 1 Transfer 0 Live in dorm 1 Full Time 0 6 Marketing 6 85 21 PC 5 3 6 2 SnapChat Yes 1 Junior 3 Male 1 Transfer 0 Live in dorm 1 Full Time 0 6 Finance 5 3 85 20 PC 3 5 6 6 No 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 20 6 7 4 95 20 PC 5 6 7 14 Yes 7 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 40 6 6 3.2 95 PC 6 6 5 30 No 6 22 Mac 5 5 4 No 3 Sophmore 2 Female 0 Montclair only 1 Commute 0 Full Time 6 Marketing 6 3.2 95 19 Mac 1 5 6 8 Instagram Yes 5 UTC
Sophmore 2 Male 1 Montclair only 1 Commute 0 Full Time 22 6 Accounting 4 95 19 PC 4 5 5 7 Instagram No 2 Sophmore 2 Female 0 Montclair only 1 Commute 0 Full Time 40 6 Finance 7 3.2 90 19 Mac 4 7 5 15 Instagram No 2 6 Marketing 5 85 19 Mac 4 5 3 10 SnapChat No 6 19 Mac 5 7 7 No 7 1 Male 1 Montclair only 1 Commute 0 Full Time 20 6 Accounting 7 5
90 19 Mac 3 4 2 20 SnapChat No 4 Freshman 1 Female 0 Montclair only 1 Live in dorm 1 Full Time 15 6 6 3.4 80 18 PC 3 5 1 15 No 5 3
18 Mac 5 5 3 No 4 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 0 5 Marketing 5 85 21 PC 5 4 4 6 Facebook No 5 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 20 5 Accounting 5 3.5 85 22 PC 5 5 4 7 Facebook No 6 Junior 3 Female 0 Transfer 0 Live in dorm 1 Full Time 30 5 Marketing 7 3.4 90 21 Neither 5 4 3 15 Facebook Yes 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 25 5 Finance 6 3.5 75 22 Mac 5 5 6 6 Instagram Yes 1 5 6 4 80 25 Mac 6 5 5 10 No 3 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 14 5 Accounting 5 3.3 80 22 PC 3 5 6 6 ReadIt No 6 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 13 5 Marketing 5 3 85 20 PC 2 5 5 3 SnapChat No 1 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 20 5 Other 7 3.1 90 23 PC 5 5 3 20 SnapChat Yes 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 33 5 Marketing 5 3.2 90 22 Mac 3 3 4 5 SnapChat Yes 3 Junior 3 Male 1 Transfer 0 Live in dorm 1 Full Time 0 5 Management 6 3 90 22 Mac 4 5 4 13 SnapChat No 2 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 15 5 Accounting 3 3.6 95 21 Mac 6 7 1 15 Twitter No 2 Junior 3 Male 1 Transfer 0 Live in dorm 1 Full Time 0 5 Finance 5 85 21 Mac 5 3 4 9 Twitter No 4 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 40 5 6 2.8 85 23 Mac 4 5 5 1 Yes 4 Junior 3 Male 1 Transfer 0 Live in dorm 1 Full Time 15 5 5 90 24 PC 5 4 3 5 Yes 6 21 PC 7 5 5 No 4 Sophmore 2 Female 0 Montclair only 1 Commute 0 Full Time 8 5 Management 6 85 18 PC 4 5 1 10 Facebook No 6 Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time 0 5 Finance 4 3.3 85 19 PC 3 5 2 8 Facebook No 2 Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time 8 5 Management 5 3.3 90 20 Mac 3 1 3 10 Facebook Yes 3 Sophmore 2 Male 1 Montclair only 1 Commute 0 Full Time 10 5 Marketing 7 85 19 Mac 2 6 6 8 Instagram No 1 Sophmore 2 Male 1 Transfer 0 Commute 0 Full Time 24 5 Marketing 5 2.9 85 20 PC 4 4 4 3 Instagram No 5 Sophmore 2 Male 1 Transfer 0 Commute 0 Full Time 25 5 Management 6 3.7 90 20 PC 5 7 3 10 Instagram No 2 Sophmore 2 Male 1 Montclair only 1 Live in dorm 1 Full Time 40 5 Management 6 3.5 90 19 Mac 4 6 6 7 Instagram No 4 Sophmore 2 Male 1 Transfer 0 Live in dorm 1 Full Time 5 5 Marketing 6 3.3 85 19 PC 5 6 4 8 No 6 Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time 0 5 Marketing 6 3.6 95 19 Mac 4 6 5 8 SnapChat No 2 Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time 0 5 Finance 7 2.9 90 19 PC 4 4 6 10 SnapChat No 5 Sophmore 2 Male 1 Montclair only 1 Live in dorm 1 Full Time 4 5 Management 6 3.5 85 19 PC 5 6 6 22 SnapChat No 4 Sophmore 2 Female 0 Transfer 0 Live in dorm 1 Full Time 24 5 Management 7 85 19 PC 4 5 4 10 SnapChat No 1 Sophmore 2 Female 0 Montclair only 1 Commute 0 Full Time 25 5 Management 5 3.7 90 19 Mac 1 5 3 10 Twitter No 3 Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time 12 5 Marketing 6 3.7 90 20 Mac 3 6 5 13 Twitter No 4 Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time 12 5 Management 5 3.3 90 19 Mac 4 6 3 10 Twitter No 4 Sophmore 2 Male 1 Transfer 0 Commute 0 Part Time 11 5 5 3.1 90 19 PC 4 7 6 4 No 7 Sophmore 2 Male 1 Montclair only 1 Commute 0 Full Time 20 5 7 3.1 90 19 Mac 4 7 5 3 No 5 Sophmore 2 Male 1 Transfer 0 Commute 0 Full Time 32 5 5 90 19 Mac 4 4 4 4 No 7 19 Mac 5 5 5 No 5 19 PC 1 5 2 No 4 Freshman 1 Male 1 Montclair only 1 Commute 0 Full Time 24 5 Accounting 6 90 18 Mac 5 5 5 25 Facebook No 3 Freshman 1 Female 0 Montclair only 1 Commute 0 Full Time 0 5 Accounting 7 95 19 Mac 4 6 5 14 SnapChat No 4 Freshman 1 Female 0 Montclair only 1 Commute 0 Full Time 20 5 Accounting 5 3.4 90 18 Mac 4 4 5 10 SnapChat No 1 Freshman 1 Male 1 Montclair only 1 Commute 0 Full Time 24 5 Management 5 80 18 Mac 3 5 5 18 Twitter No 5 Freshman 1 Female 0 Montclair only 1 Live in dorm 1 Full Time 0 5 5 2.6 85 18 Mac 4 5 2 8 No 4 Freshman 1 Male 1 Montclair only 1 Live in dorm 1 Full Time 0 5 6 95 19 PC 6 5 3 5 No 4 Freshman 1 Male 1 Montclair only 1 Live in dorm 1 Full Time 0 5 3 90 19 Mac 5 5 4 1 No 4 Senior 4 Female 0 Transfer 0 Commute 0 Full Time 50 4 Accounting 6 95 21 PC 6 6 5 16 SnapChat No 3 Senior 4 Female 0 Montclair only 1 Commute 0 Full Time 20 4 Marketing 6 3.4 90 21 PC 5 5 5 5 Twitter Yes 5 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 20 4 Other 4 85 21 PC 3 7 6 12 Facebook Yes 7 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 20 4 Management 5 3.3 90 25 PC 5 5 5 10 Facebook 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 0 4 Finance 6 90 22 PC 6 7 6 5 Instagram Yes 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 25 4 Marketing 6 3.5 85 21 PC 2 5 5 2 Instagram No 2 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 0 4 Finance 5 85 21 Mac 4 7 1 6 SnapChat Yes 4 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 30 4 Accounting 7 90 23 PC 4 3 2 5 SnapChat Yes 2 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 10 4 Management 5 2.2 85 22 PC 5 6 2 4 Twitter Yes 6 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 30 4 Management 4 3.34 85 23 Mac 4 5 1 15 Twitter No 4 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 0 4 3 2.9 90 21 PC 5 3 4 7 Yes 5 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 15 4 5 3 85 20 Mac 5 4 3 4 No 4 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 30 4 6 3.2 90 25 PC 4 6 6 10 Yes 5 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 35 4 5 3.7 75 26 Mac 5 4 6 6 Yes 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 40 4 5 3.7 85 33 PC 4 4 6 8 Yes 4 Sophmore 2 Female 0 Montclair only 1 Commute 0 Full Time 30 4 Accounting 2 3 80 20 PC 5 7 3 4 Instagram Yes 1 Sophmore 2 Male 1 Transfer 0 Commute 0 Full Time 35 4 Accounting 5 2.9 85 22 PC 2 5 1 8 Instagram Yes 4 Sophmore 2 Male 1 Montclair only 1 Commute 0 Full Time 0 4 Other 4 80 20 Neither 4 3 3 3 Twitter No 3 Sophmore 2 Male 1 Montclair only 1 Live in dorm 1 Full Time 0 4 4 2.5 80 20 Mac 3 4 5 6 Yes 1 Freshman 1 Male 1 Montclair only 1 Commute 0 Full Time 16 4 Accounting 7 85 18 Mac 5 5 5 5 Twitter No 3 Senior 4 Female 0 Transfer 0 Commute 0 Full Time 0 3 Marketing 5 2.9 85 22 PC 4 5 7 13 Instagram 7 Sophmore 2 Female 0 Transfer 0 Commute 0 Part Time 40 3 Information Management 7 90 37 Mac 6 5 4 4 Facebook 5 Freshman 1 Male 1 Transfer 0 Commute 0 Full Time 40 3 Accounting 7 3.5 85 28 PC 4 4 4 6 Newspapers/news magazines – on the web No 7 Senior 4 Male 1 Transfer 0 Commute 0 Full Time 6 2 Finance 5 3.1 90 21 Mac 3 5 2 1 SnapChat 1 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 0 2 Finance 5 2.5 85 21 PC 6 5 4 10 Facebook 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 35 2 Finance 7 85 20 PC 3 5 7 15 Facebook 5 Junior 3 Male 1 Transfer 0 Commute 0 Part Time 25 2 Finance 7 2.9 70 22 PC 4 5 3 15 Instagram Yes 4 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 30 2 Marketing 7 3.2 90 19 Mac 4 5 7 3 Instagram 5 Junior 3 Female 0 Montclair only 1 Live in dorm 1 Full Time 20 2 Finance 5 2.8 85 21 Mac 6 6 4 15 Twitter 1 Junior 3 Female 0 Transfer 0 Commute 0 Part Time 50 1 7 3.5 80 46 PC 3 6 4 3 Yes 1 Sophmore 2 Female 0 Transfer 0 Commute 0 Full Time 40 1 Management 7 3.3 95 31 PC 5 5 5 14 Facebook 6 Sophmore 2 Female 0 Transfer 0 Live in dorm 1 Full Time 0 1 Management 4 85 19 Mac 3 4 6 5 SnapChat No 1 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 14 6 Marketing 4 4 95 22 Mac 3 5 4 6 Facebook No No 3 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 0 6 Management 6 3.6 85 20 Mac 2 6 6 6 Instagram No No 4 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 14 6 Management 6 3 85 20 PC 6 5 5 7 Newspapers/news magazines – on the web Yes Yes 7 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 14 6 Management 6 3.2 75 21 PC 5 1 1 20 No No 1 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 36 6 4 2 80 21 PC 4 6 4 Other magazines – online No Yes 3 Junior 3 Female 0 Montclair only 1 Live in dorm 1 Full Time 34 6 Other 7 5
90 19 PC 3 5 3 8 No Yes 6 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 26 6 Management 6 70 25 Mac 6 6 4 15 SnapChat No No 4 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 14 6 Management 5 85 20 Mac 5 4 3 20 No Yes 5 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 20 6 Marketing 1 3.2 85 20 PC 3 3 7 10 YouTube No No 1 Sophmore 2 Male 1 Montclair only 1 Live in dorm 1 Full Time 14 6 Accounting 5 85 19 Mac 6 7 5 10 Instagram No No 4 Sophmore 2 Male 1 Montclair only 1 Commute 0 Full Time 36 6 Marketing 5 3.4 90 19 Mac 5 6 4 14 ReadIt No Yes 6 Sophmore 2 Male 1 Transfer 0 Commute 0 Full Time 0 6 Finance 5 3.4 85 23 Neither 2 2 5 7 YouTube Yes Yes 5 Senior 4 Male 1 Transfer 0 Commute 0 Full Time 30 5 Marketing 5 3.4 95 23 Mac 5 5 7 15 Facebook Yes Yes 4 Senior 4 Male 1 Transfer 0 Commute 0 Full Time 18 5 Management 5 3.5 95 21 Mac 7 7 7 5 Instagram No Yes 3 Senior 4 Male 1 Transfer 0 Commute 0 Full Time 20 5 Other 7 3.3 85 22 Mac 5 6 6 5 SnapChat No No 1 Senior 4 Female 0 Transfer 0 Commute 0 Full Time 50 5 Other 7 90 36 Mac 5 4 4 7 Twitter Yes Yes 7 Senior 4 Male 1 Transfer 0 Commute 0 Full Time 28 5 Marketing 5 3 80 22 Mac 3 3 6 Twitter No Yes 7 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 30 5 Finance 4 80 20 Mac 3 6 5 1 Facebook No No 2 Junior 3 Female 0 Transfer 0 Live in dorm 1 Full Time 0 5 Hospitality 7 3.54 95 21 Mac 4 7 6 11 Facebook Yes Yes 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 0 5 Marketing 5 3.4 85 20 PC 6 5 5 5 Facebook No Yes 7 Junior 3 Male 1 Montclair only 1 Live in dorm 1 Full Time 0 5 Other 5 80 20 PC 4 4 1 Facebook No No 5 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 0 5 Other 6 3 90 20 Mac 1 1 7 20 Instagram No No 1 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 40 5 Finance 5 3.4 85 23 Mac 4 5 5 8.00 Instagram No No 1 Junior 3 Female 0 Montclair only 1 Live in dorm 1 Full Time 10 5 Management 6 90 21 Mac 1 5 2 30 Instagram No No 2 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 22 5 Marketing 5 2.8 90 20 Mac 5 5 6 10 Instagram No Yes 7 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 30 5 Management 5 80 20 Mac 3 4 3 6 Instagram No No 2 Junior 3 Male 1 Transfer 0 Live in dorm 1 Full Time 4 5 Marketing 7 3.6 90 20 Mac 6 6 5 7 Instagram No Yes 7 Junior 3 Female 0 Transfer 0 Live in dorm 1 Full Time 16 5 Management 7 85 22 PC 3 3 6 3 LinkedIn Yes Yes 2 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 26 5 Accounting 7 3.75 95 26 Mac 7 5 5 Newspapers/news magazines – on the web No No 7 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 28 5 Marketing 5 3.3 85 20 Mac 6 6 5 5 Newspapers/news magazines – on the web No Yes 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 34 5 Management 5 95 21 PC 3 6 7 10 Newspapers/news magazines – on the web Yes Yes 3 Junior 3 Male 1 Montclair only 1 Live in dorm 1 Full Time 12 5 Marketing 6 3.7 85 20 Mac 6 7 6 14 Newspapers/news magazines – on the web No Yes 7 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 16 5 Marketing 5 3.4 85 20 Mac 4 5 4 2 Newspapers/news magazines – on the web No No 7 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 20 5 Marketing 5 3.6 90 21 Mac 5 4 3 10 Newspapers/news magazines – on the web No No 1 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 50 5 Marketing 6 3.2 95 21 PC 5 6 5 7 Other magazines – online Yes Yes 6 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 12 5 Marketing 4 3.1 90 21 Mac 5 5 4 7 Other magazines – online Yes Yes 5 Junior 3 Other 1 Transfer 0 Commute 0 Full Time 50 5 Finance 5 3.4 90 20 Neither 6 6 5 5 SnapChat No No 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 12 5 Finance 7 3.2 90 21 Mac 5 5 6 5 Twitter Yes Yes 5 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 20 5 Management 3 85 20 Mac 4 5 2 0 Twitter No No 1 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 14 5 Marketing 6 3.5 90 21 Mac 2 6 7 10 YouTube Yes Yes 5 Junior 3 Female 0 Transfer 0 Live in dorm 1 Full Time 0 5 Marketing 5 3.4 70 21 PC 5 7 4 10 YouTube No Yes 6 Sophmore 2 Male 1 Montclair only 1 Commute 0 Full Time 12 5 Other 5 95 19 Mac 6 5 5 4 Instagram No No 2 Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time 4 5 Marketing 4 3.5 85 19 Mac 4 3 4 10 Instagram No Yes 3 Sophmore 2 Female 0 Montclair only 1 Commute 0 Full Time 8 5 Management 6 3.5 95 20 PC 4 5 3 4 Newspapers/news magazines – on the web Yes Yes 5 Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time 10 5 Other 5 3.944 95 20 Mac 5 5 5 30 Pintrest No No 3 Sophmore 2 Female 0 Montclair only 1 Commute 0 Full Time 0 5 Accounting 5 85 19 PC 2 5 4 5 SnapChat No No 1 Sophmore 2 Male 1 Montclair only 1 Live in dorm 1 Full Time 0 5 Management 5 2.8 90 19 PC 4 5 7 3 SnapChat No No 1 Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time 20 5 Marketing 5 3 90 19 Mac 4 4 4 15 Twitter No Yes 7 Sophmore 2 Female 0 Montclair only 1 Commute 0 Full Time 0 5 Other 5 3.7 90 20 PC 5 5 5 2.5 Twitter Yes Yes 5 Sophmore 2 Male 1 Montclair only 1 Commute 0 Full Time 0 5 Finance 1 2.8 85 20 Mac 3 4 4 12 YouTube No No 5 Sophmore 2 Male 1 Montclair only 1 Commute 0 Full Time 0 5 Management 5 3.5 95 20 PC 6 7 7 5 YouTube No No 1 Freshman 1 Male 1 Montclair only 1 Commute 0 Full Time 0 5 Accounting 7 90 20 PC 7 7 6 12 Instagram No No 6 Senior 4 Male 1 Transfer 0 Commute 0 Full Time 40 4 Management 5 3 90 32 PC 4 6 4 6 Instagram Yes Yes 7 Senior 4 Male 1 Transfer 0 Commute 0 Full Time 20 4 Marketing 6 2.6 80 23 Mac 4 6 7 10 Newspapers/news magazines – on the web Yes Yes 4 Junior 3 Male 1 Montclair only 1 Live in dorm 1 Full Time 12 4 Marketing 7 3.3 85 21 Mac 5 5 5 10 Facebook Yes Yes 5 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 12 4 Other 7 3.6 90 20 Mac 3 3 3 4 Instagram No Yes 5 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 14 4 Finance 7 90 20 Mac 5 6 4 2 Instagram No No 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 10 4 Marketing 4 70 21 PC 2 5 2 5 Instagram Yes Yes 7 Junior 3 Male 1 Montclair only 1 Live in dorm 1 Full Time 8 4 Management 6 3.67 85 20 Mac 4 5 2 2 Instagram No No 6 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 20 4 Management 6 3.86 21 Mac 6 6 6 8 Newspapers/news magazines – on the web No No 1 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 26 4 Accounting 5 3.6 95 20 PC 6 6 4 4 Newspapers/news magazines – on the web No No 4 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 34 4 Marketing 6 3.4 85 22 PC 6 7 6 2.5 Newspapers/news magazines – on the web No No 4 Junior 3 Male 1 Montclair only 1 Commute 0 Part Time 26 4 Finance 6 2.1 85 20 PC 6 6 4 6 Newspapers/news magazines – on the web No No 4 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 28 4 Management 6 3.3 90 20 PC 5 5 6 3 Newspapers/news magazines – on the web Yes Yes 3 Junior 3 Female 0 Montclair only 1 Commute 0 Full Time 30 4 Finance 7 2.5 90 20 PC 5 4 5 5 Pintrest Yes Yes 3 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 20 4 Management 7 85 23 PC 5 6 4 2 ReadIt No No 1 Junior 3 Female 0 Montclair only 1 Live in dorm 1 Full Time 12 4 Management 7 95 20 PC 4 5 3 5 SnapChat No No 4 Junior 3 Female 0 Transfer 0 Commute 0 Full Time 40 4 Marketing 5 90 22 Mac 6 5 5 20 Twitter Yes Yes 7 Sophmore 2 Female 0 Transfer 0 Commute 0 Full Time 36 4 Accounting 7 3.6 90 22 Mac 4 4 4 10 Facebook No No 6 Sophmore 2 Female 0 Montclair only 1 Commute 0 Full Time 4 4 Accounting 5 3.5 85 20 PC 5 5 2 2 Facebook No No 7 Sophmore 2 Male 1 Montclair only 1 Commute 0 Full Time 14 4 Accounting 5 3.7 95 19 PC 4 5 5 6 Newspapers/news magazines – on the web No Yes 7 Sophmore 2 Male 1 Transfer 0 Commute 0 Full Time 20 4 Finance 5 80 20 Neither 5 4 4 5 Twitter No No 6 Sophmore 2 Male 1 Transfer 0 Commute 0 Full Time 32 4 Accounting 6 3.2 80 22 Mac 7 7 4 25 YouTube Yes Yes 5 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 36 3 Other 6 2.5 85 19 PC 5 5 6 7 Facebook Yes Yes 6 Junior 3 Female 0 Transfer 0 Commute 0 Part Time 20 3 Finance 5 3.3 90 23 PC 4 6 4 4 Instagram No No 3 Junior 3 Male 1 Transfer 0 Commute 0 Full Time 30 3 Finance 4 3.2 90 21 PC 4 4 4 0 Twitter Yes Yes 4 Junior 3 Male 1 Montclair only 1 Commute 0 Full Time 22 3 Management 5 3.4 85 20 Mac 4 4 5 7 Twitter No Yes 7 Junior 3 Male 1 Transfer 0 Commute 0 Part Time 40 2 Hospitality 7 75 33 PC 6 6 5 5 Newspapers/news magazines – on the web Yes Yes 7 >TableOfContents
with hyperlinks for this document
l data
Prep
!A for students in doing data analysis in Excel
PivotTable!A1 !A1
!A1 Regression !A1 Polygon
!A1 univariate descriptive statistics
!A1 !A1 SalaryAnalysis!A1 Optional !A1
2
MPG
Weight
4
5
9
40
1
40
8
3
7
3
30
41
15
4
50
54
6
39
20
34
3
78
38
45
40
65
25
17
51
3
56
40
95
4085
58
10
4
73
400
36
25
60
29
3
28
3
880
26
3
71
59
35
57
48
44
4
105
4
72
33
3
335
4
75
18
3895
5
150
46
3
950
43
3480
4540
360
6
32
49
3585
4
42
2810
350
3650
23
70
4
61
4
510
28
55
4
940
2590
3835
2
945
4345
3345
380
4610
3
63
3
890
3
860
3890
24
3380
4
69
3575
4
550
3
870
3430
261
3
66
47
31
2
875
37
5
705
3
67
3
91
4
82
3510
4515
4
235
3870
4
62
3625
6
245
4415
200
4025
4585
251
3085
2830
3
355
4060
3
845
5575
4845
4490
4235
3315
4
180
3
320
2690
19
3
92
3
64
21
5715
3555
4
190
4875
4550
3010
27
4480
3540
3630
2925
300
330
3015
3370
3545
3335
5015
306
3
915
2890
12
3
260
4
280
2
840
2
99
4190
3600
3280
3620
2595
4350
2950
3485
2410
3
155
3330
3
465
3245
3465
3
850
4950
22
13
16
14
PlotData
Weight (lb)
MPG
4595
17
4015
18
4085 18
3705
23
3305
25
4
11
21
450
5465
15
3
920
20
3485
24
3785
22
3885
19
4505 18
4065
5025
3
170
26
5100 15
3565
4095 19
4085 17
5810
13
4730
16
4000 18
3
68
256
27
2920
3280 21
3880 18
3
265
3710
5
93
14
5715 14
4830 15
4
420
4105 16
4
720
3350
3335 23
4
755
3
185
3895 17
5150
12
4600
3950 19
4
340
3480 15
4540 16
3605
6
325
4
905
3480 20
3585 20
4420 16
2810
31
3505
3650 21
2370
34
4615
4510
2855
4940 14
2590
28
3835 19
294
4345 18
3345 23
3
800
4610 17
3635 19
3890 19
3880 19
3860
52
3380 22
4690
3575 20
4550 15
3870 18
3430 23
2615 28
3
660
4725
4310
287
3750
570
3670
3915
4
825
3510 24
4515 21
4235 19
3870 21
4620
3625 20
6245
4415 17
2000
29
4025 18
4585 16
2510
3085 18
2830
30
3480 23
3355 23
4060 18
3845 21
5575 15
4845 16
4490 17
4235 17
3315 21
4180
3320
2690 30
4
195
3925
3640
3
215
5715 13
3555 21
4190 19
4875 15
4550 18
3010 26
2780
4480 17
3540 22
3630 20
2925 23
3000
3305 20
3015 22
3370 23
3545 21
3335 20
5015 16
3065
3915 17
2890 26
3
120
3260
3540 18
4280 16
2840
3650 18
2995
4190 17
4345 16
3600 22
3280 24
3620 21
2595 29
4350 17
4490 18
2950
44
3485 23
2410 34
3155
3330 24
3465 24
3245 23
3465 22
3850
3555 20
4950 16
SLR
Weight (lb) MPG
4015 18
4085 18
3705 23
3305 25
4
115
4505 17
5465 15
3920 20
3485 24
3785 22
3885 19
4505 18
4065 19
5025 17
317
5100 15
3565 18
4095 19
4085 17
5810 13
4730 16
4000 183
680
2560 27
2920 23
3280 21
3880 18
3265 23
3710 20
5935 14
5715 14
4830 15
4420 18
4105 16
4720 16
3350 20
3335 23
4755 173185
3895 17
5150 12
4600 16
3950 194340
3480 15
4540 16
3605 196325
4905 15
3480 20
3585 20
4420 16
2810 31
3505 21
3650 21
2370 34
4615 19
4510 17
2855 25
4940 14
2590 28
3835 19
2945 27
4345 18
3345 23
3800 18
4610 17
3635 19
3890 19
3880 19
3860 19
3890 195245
3380 22
4690 14
3575 20
4550 15
3870 18
3430 23
2615 28
3660 21
4725 17
4310 15
2875 28
3750 18
5705 13
3670 23
3915 204825
3510 24
4515 21
4235 19
3870 21
4620 16
3625 20
6245 13
4415 17
2000 29
4025 18
4585 16
2510 27
3085 18
2830 30
3480 23
3355 23
4060 18
3845 21
5575 15
4845 16
4490 17
4235 17
3315 21
4180 16
3320 23
2690 30
3345 234195
3430 23
3925 19
3640 203215
5715 13
3555 21
4190 19
4875 15
4550 18
3010 26
2780 29
4480 17
3540 22
3630 20
2925 23
3000 27
3305 20
3015 22
3370 23
3545 21
3335 20
5015 16
3065 24
3915 17
2890 263120
3260 22
3540 18
3545 21
4280 16
2840 27
3650 18
2995 22
4190 17
4345 16
3600 22
3280 24
3620 21
2595 29
4350 17
4490 18
2950 44
3485 23
4550 18
2410 34
3155 25
3330 24
3465 24
3245 23
3465 22
3850 20
3555 20
4950 16
Simple Linear
Regression
of MPG by Weight of Vehicle
Calculations
b1, b0
Coefficients
-0.0049
39.0902
Regression Statistics
b1, b0
Standard Error
0.0002
0.
97
Multiple R
0.8357
R Square
0.6
98
2.5
R Square 0.6984
F
Residual
df
391.3038
169
Adjusted R Square
0.6
96
Regression
SS
2595.9892
112
Standard Error 2.5757
Observations
171
Confidence level
95%
t Critical Value
1.9
74
ANOVA
Half Width b0
1.9318
df SS
MS
Significance F
Half Width b1
0.0005
Regression 1 2595.9892 2595.9892 391.3038
0.0000
Residual 169 1
121
6.6342
Total
371
7.1
Coefficients Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
3
9.9
37.
158
41.0
221
41.0
220
Weight (lb) -0.0049 0.0002
-1
9.7
-0.0054
-0.0044
-0.00439
DataCopy
Weight (lb)
4595
4015
4085
3705
3305
4115
4505
5465
3920
3485
3785
3885
4065
5025
3170
5100
3565
4095
5810
4730
4000
3680
2560
2920
3280
3880
3265
3710
5935
5715
4830
4420
4105
4720
3350
3335
4755
3185
3895
5150
4600
3950
4340
3480
4540
3605
6325
4905
3585
2810
3505
3650
2370
4615
4510
2855
4940
2590
3835
2945
4345
3345
3800
4610
3635
3890
3860
5245
3380
4690
3575
4550
3870
3430
2615
3660
4725
4310
2875
3750
5705
3670
3915
4825
3510
4515
4235
4620
3625
6245
4415
2000
4025
4585
2510
3085
2830
3355
4060
3845
5575
4845
4490
3315
4180
3320
2690
4195
3925
3640
3215
3555
4190
4875
3010
2780
4480
3540
3630
2925
3000
3015
3370
3545
5015
3065
2890
3120
3260
3545 4280
2840
2995
4345 3600
3620
2595
4350
2950
4550 2410
3155
3330
3465
3245
3850
4950
CompleteStatistics3
Weight
Weight (lb)
Mean
388
6.7
146
198
Median
Mode
Minimum
Maximum
Range
4325
Variance
64
161
1.5
Standard Deviation
801.0066
Coeff. of Variation
20.61%
Skewness
0.5
76
Kurtosis
0.3
268
Count
Standard Error
61.2545
DataCopy2
Asia or Europe
US
17 15
18 18
18 19
23 17
25 13
21 16
17 18
15 17
20 27
24 23
22 21
19 18
18 23
19 20
17 14
26 14
31 15
21 18
34 16
17 23
25 17
28 22
19 17
27 12
18 16
23 19
17 15
19 16
19 19
19 13
19 15
18 20
23 16
28 14
21 13
17 22
15 14
28 20
18 15
23 13
20 16
15 20
24 13
21 17
21 23
29 22
16 23
27 27
30 21
23 20
18 24
15 23
16 22
17 20
21 16
30
23
17
23
19
20
25
13
21
19
15
18
26
29
17
20
22
26
23
22
18
21
16
27
18
22
17
16
22
24
21
29
17
18
44
23
18
34
25
24
24
CompleteStats
Origin
Asia or Europe
US
Mean 21 18
Median 21 18
Mode 18 16
Minimum 13 12
Maximum 44 27
Range 31 15
Variance
23.9842
12.7341
Standard Deviation
4.8974
3.5685
Coeff. of Variation
22.99%
1
9.5
Skewness
1.4408
0.3862
Kurtosis
3.6098
-0.4992
Count
106
Standard Error
0.4757
0.4426
MPG
17
18
18
23
25
21
17
15
20
24
22
19
18
19
17
26
15
18
19
17
13
16
18
17
27
23
21
18
23
20
14
14
15
18
16
16
20
23
17
22
17
12
16
19
16
15
16
19
13
15
20
20
16
31
21
21
34
19
17
25
14
28
19
27
18
23
18
17
19
19
19
19
19
13
22
14
20
15
18
23
28
21
17
15
28
18
13
23
20
15
24
21
19
21
16
20
13
17
29
18
16
27
18
30
23
23
18
21
15
16
17
17
21
16
23
30
23
17
23
19
20
25
13
21
19
15
18
26
29
17
22
20
23
27
20
22
23
21
20
16
24
17
26
23
22
18
21
16
27
18
22
17
16
22
24
21
29
17
18
44
23
18
34
25
24
24
23
22
20
20
16
MPG
MPG
Mean 20
Median 19
Mode 18
Minimum 12
Maximum 44
Range 32
Variance
21.
865
Standard Deviation
4.6
Coeff. of Variation
2
3.2
Skewness
1.3054
Kurtosis
3.5781
Standard Error
0.3576
FiveNumbers
MPG
Five-Number Summary
First Quartile
Third Quartile
BoxPlot
12 12 12 0.5 1 1.5 17 17 17 0.5 1 1.5 19 19 19 0.5 1 1.5 23 23 23 0.5 1 1.5 44 44 44 0.5 1 1.5 12 44 1 1 17 23 0.5 0.5 17 23 1.5 1.5 ForBoxPlot
12 0.5
12 1
12 1.5
17 0.5
17 1
17 1.5
19 0.5
19 1
19 1.5
23 0.5
23 1
23 1.5
44 0.5
44 1
44 1.5
44 1
17 0.5
23 0.5
17 1.5
23 1.5
FiveNumbers 2 Gps
MPG based on
Origin of Vehicle
Asia or Europe US
First Quartile 18 16
Median
20.5
Third Quartile 24 20.5
BoxPlot 2 Gps
13 13 13 0.5 1 1.5 18 18 18 0.5 1 1.5 20.5 20.5 20.5 0.5 1 1.5 24 24 24 0.5 1 1.5 44 44 44 0.5 1 1.5 13 44 1 1 18 24 0.5 0.5 18 24 1.5 1.5
12 12 12 2 2.5 3 16 16 16 2 2.5 3 18 18 18 2 2.5 3 20.5 20.5 20.5 2 2.5 3 27 27 27 2 2.5 3 12 27 2.5 2.5 16 20.5 2 2 16 20.5 3 3 ForBoxPlot2
13 0.5 12 2
13 1 12 2.5
13 1.5 12 3
18 0.5 16 2
18 1 16 2.5
18 1.5 16 3
20.5 0.5 18 2
20.5 1 18 2.5
20.5 1.5 18 3
24 0.5 20.5 2
24 1 20.5 2.5
24 1.5 20.5 3
44 0.5 27 2
44 1 27 2.5
44 1.5 27 3
44 1 27 2.5
18 0.5 16 2
24 0.5 20.5 2
18 1.5 16 3
24 1.5 20.5 3
FiveNumbers3
Weight
Five-Number Summary
Minimum 2000
First Quartile 3335
Third Quartile 4480
BoxPlot3
2000 2000 2000 0.5 1 1.5 3335 3335 3335 0.5 1 1.5 3800 3800 3800 0.5 1 1.5 4480 4480 4480 0.5 1 1.5 6325 6325 6325 0.5 1 1.5 2000 6325 1 1 3335 4480 0.5 0.5 3335 4480 1.5 1.5 ForBoxPlot3
2000 0.5
2000 1
2000 1.5
3335 0.5
3335 1
3335 1.5
3800 0.5
3800 1
3800 1.5
4480 0.5
4480 1
4480 1.5
6325 0.5
6325 1
6325 1.5
6325 1
3335 0.5
4480 0.5
3335 1.5
4480 1.5
DataCopy4
Origin
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
US
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
US
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
US
US
US
US
US
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
US
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
US
US
US
US
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
US
US
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
US
US
US
US
Asia or Europe
Asia or Europe
US
US
US
US
US
US
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
Asia or Europe
US
US
US
US
US
Bar Chart
Total Asia or Europe US 106 65 OriginOneWayTable
Origin of Vehicle
Count of Origin
Origin Total
Asia or Europe 106
US 65
Grand Total
SideBySide
4 Door Hatchback
4 Door SUV
Coupe
Minivan
Sedan
Wagon
TwoWayTable
Origin and Auto Type
Count of Origin
Type Auto
Origin 4 Door Hatchback 4 Door SUV Coupe Minivan Sedan Wagon Grand Total
Asia or Europe 6 33 17 4 40 6 106
US 1 27 6 3 25 3 65
Grand Total 7 60 23 7 65 9 171
Variable INFO
Automobile features taken from a sample of 171 car models
Source
Berenson, Levine, Krehbiel Basic Business Statistics: Concepts and Applications (12 ed.), Pearson
Variables
Make/Model
MPG
Miles Per Gallon
Length
CargoWidth
Cargo storage width in inches
Height
WheelBase
Wheel base distance in inches
MaxLoad
Maximum car load in pounds
CargoVol
Volume of trunk space in cubic inches
feet
HP
Horsepower
Accto60
Time in seconds to accelerate to 60 miles per hour
Breakfr60
Distance in feet to stop vehicle when applying the breaks at 60 miles per hour
TrngCirc
Distance in feet to turn the vehicle 360 degrees
Origin
US, Asia or Europe
TypeAuto
Type of automobile
Transmission
Type of transmission
DATA
Automobile comparison studey data
Make/Model MPG
Length (in)
CargoWidth (in)
Height (in)
WheelBase (in)
MaxLoad (lb)
CargoVol(cuft)
Accto60(sec)
Breakfr60(ft)
TrngCirc(ft)
Acura MDX
191
79
108
116
139
Auto 5
Acura RDX
181
104
240
7.4
145
Acura RL
194
110
6.9
148
Acura TL
109
270
138
Audi A3
77
102
990
7.3
149
Seq 6
Audi A6
1100
255
7.7
144
Auto 6
Audi A8
204
1
210
7.6
140
Audi Q7
118
132
8.2
Audi S4
114
5.3
Man 6
BMW 3 Series sedan 328i 6-cyl
178
1060
142
BMW 5 Series
5.8
BMW 6 Series
5.6
133
BMW 7 Series
123
154
BMW X3
1005
7.9
BMW X5
135
8.6
BMW Z4
225
126
Buick Enclave
201
119
1335
275
8.9
1
53
Buick LaCrosse
111
160
Auto 4
Buick Lucerne
203
197
9.2
175
Cadillac DTS
208
1095
Cadillac Escalade
1330
403
7.5
Cadillac SRX
1285
8.3
152
Cadillac STS
196
Cadillac XLR
6.4
Chevrolet Aveo hatchback 1LT 4-cyl
103
11.2
Man 5
Chevrolet Cobalt sedan LT 4-cyl
8.8
Chevrolet Corvette convertible Base V8 MT
390
Chevrolet Equinox
189
113
1115
9.1
153
Chevrolet HHR
176
820
172
159
Chevrolet Impala
242
7.8
Chevrolet Suburban
222
130
1460
165
Chevrolet Tahoe LT V8
202
1580
168
Chevrolet TrailBlazer
192
1020
291
Chevrolet Uplander
205
141
Chrysler 300 C V8
Chrysler Pacifica
199
1000
253
Chrysler PT Cruiser
8.1
Chrysler Sebring sedan Touring 4-cyl
173
10.2
156
Chrysler Town & Country
1150
Dodge Caliber SXT 4-cyl
CVT
174
10.3
157
Dodge Charger SXT V6
134
Dodge Durango
1455
167
Dodge Grand Caravan
182
Dodge Magnum
250
166
Dodge Nitro
179
Dodge Viper
4.2
Ford Edge
186
910
Ford Escape
10.5
Ford Expedition
131
1570
Ford Explorer XLT V6
193
127
163
Ford Fusion SEL V6
107
143
Ford Mustang coupe GT Premium V8 MT
188
5.5
Ford Taurus X
263
8.5
Honda Civic sedan EX 4-cyl
177
136
Honda CR-V
10.6
Honda Element
101
675
10.4
162
Honda Fit Sport 4-cyl MT
Honda Odyssey
1320
Honda Pilot
Honda S2000
Hummer H3
187
11.5
Hyundai Accent GLS 4-cyl
12.5
Hyundai Azera
Hyundai Elantra GLS 4-cyl
Hyundai Santa Fe
184
1120
Hyundai Sonata GLS 4-cyl
Hyundai Tucson
10.1
Hyundai Veracruz
1160
Infiniti G sedan Journey V6
900
2.2
Infiniti M M35 V6
Jaguar S-Type
6.3
Jaguar XJ
Jaguar XK
Jeep Commander
Jeep Compass
Jeep Grand Cherokee
1050
Jeep Patriot
10.8
Jeep Wrangler
10.7
Kia Amanti
Kia Optima EX V6
Kia Rio sedan LX 4-cyl
12.8
Kia Rondo
9.6
Kia Sedona
1155
244
Kia Sorento
Kia Spectra EX 4-cyl
Kia Sportage
11.3
Land Rover LR3
147
Lexus ES
272
Lexus GS 300 V6
815
Lexus GX
122
Lexus IS
Lexus LS
6.2
Auto 8
Lexus RX 350 V6
Lexus SC
645
288
6.5
Lincoln MKX
Lincoln MKZ
Lincoln Navigator
1
525
Lincoln Town Car
239
8.7
Lotus Elise
Mazda CX-7
Mazda CX-9
1190
Mazda MX-5 Miata
Mazda RX-8
238
Mazda Mazda3 sedan i 4-cyl MT
Mazda Mazda5
Mazda Mazda6
Mercedes-Benz CLS
302
6.1
Auto 7
Mercedes-Benz E-Class sedan E350 V6
1010
Mercedes-Benz GL-Class
1210
Mercedes-Benz M-Class
1165
Mercedes-Benz S-Class
125
1135
382
Mercedes-Benz SL
Mercedes-Benz SLK
Mercury Grand Marquis
212
151
Mercury Milan Base 4-cyl
Mini Cooper
7.2
Mitsubishi Eclipse hatchback GS 4-cyl MT
9.3
Mitsubishi Endeavor
970
Mitsubishi Galant ES 4-cyl
Mitsubishi Outlander XLS V6
183
Nissan 350Z
Nissan Altima sedan 2.5 S 4-cyl CVT
Nissan Armada
1375
Nissan Maxima
6.8
Nissan Murano
Nissan Pathfinder
1125
Nissan Quest
124
1205
8.4
Nissan Sentra 2.0 S 4-cyl CVT
164
Nissan Versa hatchback 1.8 S 4-cyl MT
Nissan Xterra
Pontiac G6 sedan GT V6
9.4
Pontiac Grand Prix
Pontiac Solstice
Pontiac Vibe
Porsche 911
4.4
Porsche Boxster
Saab 9-3 sedan 2.0T 4-cyl
930
Saab 9-5
Saturn Aura XE 4-cyl
224
Saturn Outlook
Saturn Sky
425
Saturn Vue
257
Scion tC
Scion xB
Subaru Forester 2.5X 4-cyl
Subaru Legacy
3.1
Subaru Outback wagon 2.5i 4-cyl
11.8
Subaru Tribeca
Suzuki Forenza
Suzuki Grand Vitara
Suzuki SX4 Sport 4-cyl
12.2
Suzuki XL-7
252
Toyota 4Runner
1035
Toyota Avalon
Toyota Camry LE 4-cyl
Toyota Camry Solara
Toyota Corolla Base 4-cyl MT
9.8
Toyota FJ Cruiser
Toyota Highlander Limited V6
1200
Toyota Prius Base 4-cyl CVT
Toyota RAV4 Base 4-cyl
Toyota Sienna XLE AWD V6
266
Toyota Yaris hatchback Base 4-cyl
Volkswagen GTI
960
Volkswagen Jetta 2.5 5-cyl
Volkswagen Passat
975
Volvo S40
Volvo S60
Volvo S80
Volvo V50
218
Volvo XC90
311
2
Variables and measures
Count=
1
9
Surveys from semesters Fall 1
6
7
8
0
Variable label
Variable full reference
Scale (if any)
type of variable
date
When the survey was taken
na
nominal
Class
What is your class designation?
nominal or
ordinal
Gender
What is your gender?
Transfer
Is Montclair the only college you have attended,or are you a transfer student?
FullPartTime
Are you a full-time or part-time student at MSU?
ClassesTaking
How many
3
HoursWork
How many hours/week do you work? Answer with an average. Please use only numbers, e.g.
5
11
What is your major
What is your major?
ChoiceOf
Major
How strongly do you feel about your choice of major?
On a scale of 1 to 7, 1 is uncertain and 7 is very enthousiatic/passionate.
HSAverage
What is your overall average high school grade?
numeric
MontclairGPA
What is your Montclair GPA?
Age
What is your age? Use numbers only.
ComputerType
Is your primary school computer a
PC
Mac
ConfidentStats
How confident are you of your mastery of business statistics now that you have taken INFO 2
4
1 is not at all confident and 7 is extremely confident.
ConfidentWriting
How confident are you of your mastery of business writing now that you have taken the prerequisite writing course?
ConfidentSpeaking
How confident are you of your mastery of public speaking?
InterestInCampaign
How interested are/were you in the
20
16
1 is not at all interested and 7 is extremely passionate.
Party
What political party do you identify with?
HaveVoted
Have you ever voted?
WillVote
Will you vote in the presidential election?
HoursStudyPerWeek
How many hours do you study for all courses per week on average?
Commute
Do you commute or live in a dorm?
categorical
Media
Which media do you use the most?
StudentSurveyData
section
section_id
submitted
47
19
45
4719454: What is your gender?
4719455: Is Montclair the only college you have attended, or are you a transfer student?
4719
46
4719456: Are you a full-time or part-time student at MSU?
4719458: How many hours/week do you work for pay?  Answer with an average. Â
4719457: How many 3-credit classes are you taking this semester?
4719459: What is your major?
47194
60
4719462: What is your Montclair GPA?
4719461: What is your overall average high school grade?
4719463: What is your age? Use numbers only.
4719464: Is your primary school computer a PC or Mac?
4719465: How confident are you of your mastery of business statistics now that you have taken INFO 2
40
4719466: How confident are you of your mastery of business writing now that you have taken the prerequisite writing course?  1 is not at all confident and 7 is extremely confident.
4719467: How confident are you of your mastery of public speaking?  1 is not at all confident and 7 is extremely confident.
4719468: How many hours a week do you study on average? Please use numbers only.
47194
70
Which media do you use the most?
Check the box next to each media you use at least a couple of times a week.
4719471: Did you vote in the last presidential election?
4719472: Have you ever voted?
ElectionInterest
NOTICE THERE IS AN EMPTY ROW BELOW THIS ROW – AN EMPTY ROW/COLUMN AROUND A CONTINUOUS BODY OF DATA IS AUTOMATICALLY RECOGNIZED BY EXCEL AS THE DATA YOU ARE GOING TO ADDRESS WITH A PIVOT TABLE (UNLESS YOU HAVE HIGHLIGHTED A FEW CELLS – SO JUST CLICK ON ONE CELL IN THE BODY OF THE DATA TO START A PIVOT TABLE.
section section_id submitted Class
ClassNum
GenderNum
TransferNum
CommuteDormNum
FTPT
HoursPay
ClassesNum
MajorFeel
MSUGPA
PCMac
ConfStats
ConfWriting
ConfSPeaking
HoursStudyWeek
SocialMediaTop
VoteLastElection
EverVoted
BUGN2
80
22
Junior
Male
Full Time
3.
75
21
Yes
BUGN
28
Female
Montclair only
30
2.1
No
BUGN280_22SP16 Junior 3 Male 1 Transfer 0 Full Time 40 4 6
3.2
BUGN280_22SP16 Junior 3 Male 1 Transfer 0 Full Time 40 4 6
2.9
31
BUGN280_22SP16 Junior 3 Female 0 Transfer 0 Full Time
50
2.8
BUGN280_22SP16 Junior 3 Male 1 Transfer 0 Full Time 50 4 7 3.2
24
BUGN280_22SP16
Sophmore
3.3
BUGN280_22SP16
Senior
33
BUGN280_22SP16 Junior 3 Male 1 Transfer 0 Full Time 0 3 7 2.9 22 Mac 7 7 7 Yes 7
BUGN280_22SP16 Junior 3 Male 1 Transfer 0
Part Time
3.4
29
BUGN280_22SP16 Junior 3 Female 0 Transfer 0 Part Time 60 3 7 3 24 PC 4 6 7 Yes 7
BUGN280_22SU16
2016-06-
13
26
Marketing
3.
27
90
34
Facebook
BUGN280_13SP16
2016-01-28 19:46:01 UTC
38
Management
85
10
Instagram
BUGN280_22SU16
2016-06-13 22:31:04 UTC
Live in dorm
BUGN280_13SP16
2016-01-28 19:48:01 UTC
Other
2.6
BUGN280_
15
2016-01-27 22:40:
36
Accounting
3.26
BUGN280_13SP16
2016-01-28 19:50:11 UTC
BUGN280_15SP16
2016-01-27 22:43:30 UTC
18
95
BUGN280_04SP16
2016-01-28 15:
23
35
3.
14
BUGN280_13SP16
2016-01-28 19:47:01 UTC
Finance
3.1
ReadIt
BUGN280_15SP16
2016-01-27 22:44:13 UTC
3.
32
Neither
SnapChat
BUGN280_04SP16
2016-01-28 15:22:51 UTC
3.5
BUGN280_04SP16
2016-01-28 15:23:24 UTC
Twitter
BUGN280_16SP16
2016-01-26 23:15:52 UTC
BUGN280_16SP16
2016-01-26 23:15:10 UTC
25
BUGN280_22SP16 Junior 3 Female 0 Montclair only 1 Full Time 20 6 5 2.8 20 Mac 5 4 4 No 4
BUGN280_22SP16 Junior 3 Female 0 Transfer 0 Full Time 30 6 6
3.95
BUGN280_04SP16
2016-01-28 15:22:20 UTC
17.5
BUGN280_15SP16
2016-01-27 22:43:
37
3.52
BUGN280_13SP16
2016-01-28 19:46:53 UTC
BUGN280_04SP16 2016-01-28 15:22:51 UTC Sophmore 2 Female 0 Montclair only 1 Live in dorm 1 Full Time
12
2.7
BUGN280_22SP16 Sophmore 2 Male 1 Montclair only 1 Full Time 30 6 6
3.34
BUGN280_13SP16
2016-01-28 19:48:50 UTC
Freshman
3.7
BUGN280_16SP16
2016-01-26 23:14:21 UTC
BUGN280_22SP16 Freshman 1 Male 1 Montclair only 1 Full Time 0 6 6
3.6
BUGN280_13SP16
2016-01-28 19:47:00 UTC
2.4
BUGN280_04SP16
2016-01-28 15:25:40 UTC
BUGN280_15SP16
2016-01-27 22:46:52 UTC
BUGN280_13SP16
2016-01-28 19:45:06 UTC
BUGN280_15SP16 2016-01-27 22:44:13 UTC Junior 3 Male 1 Transfer 0 Commute 0 Full Time
4
2.5
Information Management
LinkedIn
BUGN280_15SP16
2016-01-27 22:41:28 UTC
BUGN280_13SP16
2016-01-28 19:53:00 UTC
BUGN280_13SP16
2016-01-28 19:48:15 UTC
BUGN280_15SP16
2016-01-27 22:41:08 UTC
BUGN280_13SP16
2016-01-28 19:47:15 UTC
BUGN280_13SP16
2016-01-28 19:44:33 UTC
BUGN280_13SP16
2016-01-28 19:51:39 UTC
3.49
BUGN280_16SP16
2016-01-26 23:15:04 UTC
BUGN280_16SP16
2016-01-26 23:16:07 UTC
3.09
BUGN280_22SP16 Junior 3 Male 1 Transfer 0 Full Time 17.5 5 6 2.8 22 PC 4 5 6 Yes 1
BUGN280_22SP16 Junior 3 Female 0 Transfer 0 Full Time 27 5 7
3.54
BUGN280_22SP16 Junior 3 Male 1 Montclair only 1 Full Time 30 5 6 3.2 21 Mac 4 5 5 Yes 6
BUGN280_22SP16 Junior 3 Male 1 Montclair only 1 Full Time 40 5 7 2.8 21 PC 5 5 7 No 5
BUGN280_22SP16 Junior 3 Male 1 Montclair only 1 Full Time 45 5 5 3 21 PC 6 4 5 No 5
BUGN280_04SP16
2016-01-28 15:24:07 UTC
3.48
BUGN280_15SP16
2016-01-27 22:41:34 UTC
BUGN280_15SP16
2016-01-27 22:42:27 UTC
BUGN280_04SP16
2016-01-28 15:23:29 UTC
3.544
BUGN280_13SP16
2016-01-28 19:54:34 UTC
BUGN280_04SP16
2016-01-28 15:22:33 UTC
BUGN280_04SP16
2016-01-28 15:23:42 UTC
BUGN280_04SP16
2016-01-28 15:22:52 UTC
Newspapers/news magazines – on the web
BUGN280_15SP16
2016-01-27 22:43:29 UTC
BUGN280_15SP16
2016-01-27 22:41:35 UTC
BUGN280_15SP16
2016-01-27 22:42:56 UTC
BUGN280_04SP16
2016-01-28 15:24:54 UTC
BUGN280_04SP16
2016-01-28 15:22:11 UTC
BUGN280_15SP16
2016-01-27 22:43:17 UTC
BUGN280_13SP16
2016-01-28 19:45:37 UTC
BUGN280_16SP16
2016-01-26 23:17:02 UTC
BUGN280_16SP16
2016-01-26 23:15:21 UTC
BUGN280_16SP16
2016-01-26 23:14:30 UTC
3.67
BUGN280_22SP16 Sophmore 2 Female 0 Montclair only 1 Full Time 15 5 5 3.2 19 Mac 4 5 4 No 4
BUGN280_22SP16 Sophmore 2 Female 0 Montclair only 1 Full Time 20 5 5
3.8
BUGN280_22SP16 Sophmore 2 Female 0 Montclair only 1 Full Time 24 5 5 3.1 19 PC 1 5 1 No 6
BUGN280_22SP16 Sophmore 2 Female 0 Montclair only 1 Full Time 25 5 3
3.314
BUGN280_13SP16
2016-01-28 19:48:37 UTC
3.76
BUGN280_13SP16
2016-01-28 19:50:35 UTC
3.944
BUGN280_04SP16
2016-01-28 15:22:32 UTC
BUGN280_04SP16
2016-01-28 15:24:01 UTC
2.3
BUGN280_16SP16
2016-01-26 23:15:32 UTC
BUGN280_16SP16
2016-01-26 23:15:44 UTC
3.55
BUGN280_16SP16
2016-01-26 23:15:23 UTC
3.006
BUGN280_15SP16
2016-01-27 22:41:47 UTC
3.45
BUGN280_04SP16
2016-01-28 15:22:27 UTC
BUGN280_13SP16
2016-01-28 20:00:01 UTC
2.2
BUGN280_22SU16
2016-06-13 22:24:25 UTC
BUGN280_04SP16
2016-01-28 15:23:21 UTC
BUGN280_15SP16
2016-01-27 22:41:56 UTC
BUGN280_04SP16
2016-01-28 15:24:57 UTC
3.08
BUGN280_15SP16
2016-01-27 22:41:51 UTC
BUGN280_13SP16
2016-01-28 19:48:28 UTC
BUGN280_15SP16
2016-01-27 22:45:13 UTC
BUGN280_16SP16
2016-01-26 23:19:11 UTC
BUGN280_16SP16
2016-01-26 23:16:05 UTC
BUGN280_16SP16
2016-01-26 23:15:43 UTC
BUGN280_16SP16
2016-01-26 23:14:38 UTC
BUGN280_16SP16
2016-01-26 23:15:02 UTC
BUGN280_13SP16
2016-01-28 19:46:11 UTC
BUGN280_15SP16
2016-01-27 22:41:22 UTC
BUGN280_13SP16
2016-01-28 19:46:23 UTC
2.89
BUGN280_16SP16
2016-01-26 23:16:28 UTC
BUGN280_13SP16
2016-01-28 19:46:43 UTC
2.495
BUGN280_22SU16
2016-06-13 22:23:54 UTC
BUGN280_22SU16
2016-06-13 22:30:32 UTC
3.85
BUGN280_13SP16
2016-01-28 19:50:46 UTC
BUGN280_22SU16
2016-06-13 22:24:11 UTC
BUGN280_22SU16
2016-06-13 22:25:37 UTC
BUGN280_22SU16
2016-06-13 22:25:49 UTC
2.88
BUGN280_15SP16
2016-01-27 22:42:05 UTC
BUGN280_22SU16
2016-06-13 22:25:06 UTC
BUGN280_22SU16
2016-06-13 22:28:29 UTC
BUGN280_16SP16
2016-01-26 23:17:20 UTC
BUGN280_22SU16
2016-06-13 22:26:10 UTC
BUGN280_04SP16
2016-01-28 15:21:55 UTC
2.94
BUGN280_08FA19
110757
2019-10-26 04:37:40 UTC
BUGN280_08FA19 110757
2019-10-28 01:06:51 UTC
BUGN280_10FA19
110764
2019-10-28 12:08:36 UTC
BUGN280_12FA19
110795
2019-10-28 15:15:29 UTC
Other magazines – online
BUGN280_10FA19 110764
2019-10-28 18:36:48 UTC
Hospitality
6.5
BUGN280_08FA19 110757
2019-10-26 15:53:57 UTC
3.86
Pintrest
BUGN280_12FA19 110795
2019-10-27 23:52:39 UTC
2.95
BUGN280_08FA19 110757
2019-10-29 18:08:40 UTC
3.545
YouTube
BUGN280_08FA19 110757
2019-10-27 21:38:15 UTC
BUGN280_03FA19
110743
2019-10-26 12:16:34 UTC
3.356
BUGN280_10FA19 110764
2019-10-28 00:58:40 UTC
BUGN280_12FA19 110795
2019-10-26 01:10:39 UTC
BUGN280_03FA19 110743
2019-10-27 18:31:01 UTC
BUGN280_12FA19 110795
2019-10-28 03:10:25 UTC
BUGN280_08FA19 110757
2019-10-27 20:28:24 UTC
BUGN280_03FA19 110743
2019-10-28 02:13:20 UTC
3.75
BUGN280_12FA19 110795
2019-10-28 02:12:49 UTC
7.00
BUGN280_08FA19 110757
2019-10-27 23:28:54 UTC
3.53
BUGN280_03FA19 110743
2019-10-27 19:30:16 UTC
BUGN280_03FA19 110743
2019-10-26 19:58:03 UTC
BUGN280_12FA19 110795
2019-10-27 22:52:04 UTC
3.421
8.00
BUGN280_10FA19 110764
2019-10-28 00:48:32 UTC
BUGN280_12FA19 110795
2019-10-28 01:07:42 UTC
BUGN280_03FA19 110743
2019-10-28 03:33:37 UTC
3.308
BUGN280_08FA19 110757
2019-10-26 14:45:17 UTC
BUGN280_08FA19 110757
2019-10-27 21:06:24 UTC
3.58
BUGN280_10FA19 110764
2019-10-27 21:37:09 UTC
BUGN280_08FA19 110757
2019-10-27 14:32:08 UTC
3.017
BUGN280_03FA19 110743
2019-10-28 03:55:03 UTC
13.00
BUGN280_08FA19 110757
2019-10-28 01:39:43 UTC
BUGN280_03FA19 110743
2019-10-26 14:50:55 UTC
3.82
BUGN280_03FA19 110743
2019-10-27 19:14:46 UTC
BUGN280_03FA19 110743
2019-10-27 07:00:45 UTC
BUGN280_10FA19 110764
2019-10-25 19:48:28 UTC
BUGN280_10FA19 110764
2019-10-28 02:21:41 UTC
BUGN280_03FA19 110743
2019-10-27 16:13:08 UTC
BUGN280_12FA19 110795
2019-10-27 11:38:38 UTC
BUGN280_10FA19 110764
2019-10-27 22:09:54 UTC
BUGN280_08FA19 110757
2019-10-27 19:51:39 UTC
3.56
BUGN280_12FA19 110795
2019-10-27 17:52:09 UTC
BUGN280_12FA19 110795
2019-10-26 14:29:28 UTC
BUGN280_10FA19 110764
2019-10-26 16:22:45 UTC
2.556
BUGN280_03FA19 110743
2019-10-28 02:50:13 UTC
BUGN280_03FA19 110743
2019-10-27 15:22:05 UTC
BUGN280_08FA19 110757
2019-10-27 23:45:04 UTC
BUGN280_08FA19 110757
2019-10-26 19:47:02 UTC
3.739
BUGN280_08FA19 110757
2019-10-26 05:29:55 UTC
BUGN280_12FA19 110795
2019-10-28 03:20:29 UTC
BUGN280_03FA19 110743
2019-10-28 14:10:48 UTC
BUGN280_03FA19 110743
2019-10-26 20:56:29 UTC
BUGN280_03FA19 110743
2019-10-26 05:25:39 UTC
BUGN280_08FA19 110757
2019-10-26 17:46:58 UTC
3.813
BUGN280_12FA19 110795
2019-10-28 12:53:22 UTC
BUGN280_03FA19 110743
2019-10-27 16:42:20 UTC
BUGN280_03FA19 110743
2019-10-28 03:02:05 UTC
BUGN280_08FA19 110757
2019-10-28 00:00:46 UTC
BUGN280_12FA19 110795
2019-10-28 03:55:14 UTC
3.18
BUGN280_10FA19 110764
2019-10-26 19:33:11 UTC
3.17
BUGN280_03FA19 110743
2019-10-28 03:04:49 UTC
BUGN280_03FA19 110743
2019-10-26 19:14:17 UTC
100
BUGN280_10FA19 110764
2019-10-27 21:22:16 UTC
BUGN280_10FA19 110764
2019-10-27 18:58:12 UTC
BUGN280_10FA19 110764
2019-10-28 02:16:00 UTC
BUGN280_10FA19 110764
2019-10-26 17:48:48 UTC
BUGN280_08FA19 110757
2019-10-28 03:48:41 UTC
BUGN280_10FA19 110764
2019-10-29 03:13:25 UTC
2.57
BUGN280_08FA19 110757
2019-10-26 15:03:05 UTC
3.92
BUGN280_12FA19 110795
2019-10-27 22:47:00 UTC
1.87
BUGN280_10FA19 110764
2019-10-27 11:59:31 UTC
BUGN280_12FA19 110795
2019-10-26 20:30:23 UTC
BUGN280_03FA19 110743
2019-10-26 20:26:21 UTC
BUGN280_10FA19 110764
2019-10-28 00:05:42 UTC
1.9
BUGN280_12FA19 110795
2019-10-27 21:14:56 UTC
BUGN280_12FA19 110795
2019-10-28 14:47:53 UTC
BUGN280_12FA19 110795
2019-10-27 03:31:53 UTC
BUGN280_12FA19 110795
2019-10-25 21:20:59 UTC
BUGN280_10FA19 110764
2019-10-27 23:30:57 UTC
BUGN280_10FA19 110764
2019-10-27 17:44:31 UTC
3.68
2
Table of contents
Excluding standard worksheets that come with the origi
na
Sheet name
Purpose
NotesOn
Data
1
Tips and tricks
Salary
Using a histogram of salary to compare other variables in terms of chunks of salary
DescriptiveStatsFor
Frequency
Example of producing descriptive stats for chunks of a numeric variable (grouping, frequency table as ‘categories’)
VariableDescriptiveStatsPHStat
Example of descriptive stats produced by PHStat and then edited, items removed that are not needed
Correlations!A1
Instructor reference for how all variables are inter-related
Age
Example of regression output highighting output to pay attention to
SPSSRegressionAllEnter!A1
Instructor reference – regressing salary on all independent variables to discern stongest, independent predictors
PivotTableCreate
Percent
Example of comparing distributions between two categories with different number of cases or different scales, i.e., version of percent polygon
Analysis results
Gender
GenderAnalysis
Gender/Salary; Gender/Job Grade Classification analysis; Gender/other independent variables
Salary histogram, distribution
Compare gender/salary descriptive statistics
GenderCompareDescriptives
Comparison Table gender descriptive statistics in terms of all variables. This might be something worth doing.
Ethnicity
Ethnicity/Salary analysis
EthnicitySalaryAnalysis
Optional ethnicity/salary analysis – distribution of ethnicity over chunks of salary, percent polygon
Ethnicity
Analysis
!A1
AgeSalaryAnalysis
!A1
AgeJobGradeClassAnalysis
!A1
YearsWorkedSalaryAnalysis
!A1
/Salary analysis
PercentPolygonGenderYearsWorked
!A1
distribution by gender; Example of comparing distributions between two categories with different number of cases or different scales, i.e., version of percent polygon
Variable INFO
‘!A1
Human Resources DATA
‘!A1 Data
Cross-Class-Table
‘!A1
Sum
mary Table
‘!A1
Histogram
!A1
% Polygons 2 Groups
‘!A1
Freq. & % Distribution
‘!A1
Variable INFO
TableOfContents!A1 |
responses to a survey conducted by the VP of Human Resources at a large company.
01 class at Montclair State University
in years
, 5,
,
,
(lowest skill job to highest skill job)
,
)
s created in this worksheet – use these names to address the data more quickly then manually selecting data
1.
75
7
465.
1
91
male salary
%
Human Resources DATA
EthnicityCODE | Gender code | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 31 | 19 | Female | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 40 | 28 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 42 | 60 | 29 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 39 | 26 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 33 | 30 | 22 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 35 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 34 | 38 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 43 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 37 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$34,600 | 27 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$37, | 70 | 36 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 48 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$38,900 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 58 | 49 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 52 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,500 | 45 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$52,300 | 47 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$50,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 54 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 57 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,700 | 62 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$49,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$70,100 | 53 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$60,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$48,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$57,000 | 61 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$57,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,800 | 44 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,600 | 51 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 59 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 72 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$43,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$70,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$54,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 55 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$67,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
15 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$38,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$62,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$65,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$43,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$67,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$56,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$39,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$39,200 | 14 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$58,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$39,800 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
66 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 68 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
24 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$33,400 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$42,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$56,300 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$45,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$48,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$54,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$50,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,800 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$44,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$56,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$37,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$45,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$45,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$62,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
41 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$48,100 | 32 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$75,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$66,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$62,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$53,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$54,900 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$53,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$43,300 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,400 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$64,300 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$61,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$38,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$56,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$60,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$64,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$52,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$79,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$76,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$62,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$72,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$61,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$68,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 82 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$67,800 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$81,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
16 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$77,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$68,000 | 63 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 73 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
69 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$76,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$69,500 | 18 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$39,900 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$64,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
74 |
NotesOnDataPrep
1. It will make the student’s life easier to create named ranges in the data for the ranges they need. Simply sort, highlight the range, and in the box upper left, type in a name. Use that name in functions and formulas (e.g., quartile(), or descriptive stats – you can use named ranges in PHStat and Data Analysis Toolpack) | ||
2. Note that Pivot tables can provide all descriptive statistics except median, quartiles, IQR. If Zscores indicate that there is an outlier on one side, students should not be using the mean, but as a work around, you can ask them to note that, discuss what it means and then use the mean/SD anyway; OR you can require them to manually create those separately from the pivot table (or don’t use a pivot table, use the data analysis toolpack or PHSTat). | ||
3. | Instructions | |
a. Create a pivot table using the numeric variable (age) as the row label | ||
b. Group the row label – Group button on ribbon. Choose chunks in dialog box. | ||
make sure you click in the data, not the header, or the button will be greyed out | ||
Play with the beginning, end value and chunks to make | bins | |
c. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item. | ||
d. To work with data, it is frequently easier to copy pivot table data and paste as – paste as values. | ||
e. Word of warning: If you divide data into subcategories – chunks of salary for women, men – if there are no values for a category, Excel won’t list it – you have to manually put a zero in for the value. | ||
4. Getting Excel stuff into Word for a report: It might be easier to paste as a picture object – easier to manipulate. |
PercentDifference
Count | Column Labels | ||||||||||||||||||||||||||||||||||||||||||||||||||
Row Labels | Grand | Total | |||||||||||||||||||||||||||||||||||||||||||||||||
16.98% | 5.97% | 10.8 | 3% | ||||||||||||||||||||||||||||||||||||||||||||||||
33.9 | 6% | 2 | 5.3 | 29. | 17% | ||||||||||||||||||||||||||||||||||||||||||||||
96% | 35.8 | 2% | 35.0 | 0% | |||||||||||||||||||||||||||||||||||||||||||||||
1 | 3.2 | 22.39% | 18. | 33% | |||||||||||||||||||||||||||||||||||||||||||||||
1.89% | 1 | 0.4 | 5% | 6.6 | |||||||||||||||||||||||||||||||||||||||||||||||
Grand Total | 10 | 0.00 | 10 | 0.00% | Count of Gender | ||||||||||||||||||||||||||||||||||||||||||||||
3 | 4% | 25 | 29% | ||||||||||||||||||||||||||||||||||||||||||||||||
34% | 36% | ||||||||||||||||||||||||||||||||||||||||||||||||||
13% | 22% | 52% | |||||||||||||||||||||||||||||||||||||||||||||||||
10% | 139% |
Percent difference in Male to
Proportions
3 5 7 9 11 0.95950920245398763
8951115329852861 5.326876513
1914E-2
1582278481012667 1.3881278538812787 3 5 7 9 11 3 5 7 9 11
3 5 7 9 11 1
Job Levels
LevelGender
Average of Salary | ||||||||||
38484.6153846154 | ||||||||||
3755 | 5.55 | |||||||||
40575 | ||||||||||
4634 | 5.71 | |||||||||
45250 | ||||||||||
47505.8823529412 | ||||||||||
54809.5238095238 | ||||||||||
57194.4444444444 | ||||||||||
53020.8333333333 | ||||||||||
65740.9090909091 | ||||||||||
62371.4285714286 | ||||||||||
67313.3333333333 | ||||||||||
74825 | ||||||||||
67500 | ||||||||||
75871.4285714286 | ||||||||||
53910.8333333333 |
Average salary
of gender on each level
Total
Female Male Female Male Female Male Female Male Female Male 3 5 7 9 11 37555.555555555555 40575 45250 47505.882352941175 57194.444444444445 53020.833333333336 62371.428571428572 67313.333333333328 67500 75871.428571428565
BiVariateDistributionChart
30000-39999 | 36938.4615384615 | 37800 | 37177.7777777778 | ||||||||||||||||
40000-49999 | 45878.5714285714 | 45680 | 4576 | 1.7 | |||||||||||||||
50000 | 55533.3333333333 | 543 | 21 | 54948.275862069 | |||||||||||||||
60000 | 648 | 12.5 | 64578.9473684211 | 64648.1481481481 | |||||||||||||||
70000 | 70700 | 75671.4285714286 | 74180 | ||||||||||||||||
80000-90000 | 81700 | ||||||||||||||||||
50681.1320754717 | 5646 | 5.67 |
Gender average salary comparison by salary level
Female 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 36938.461538461539 4587 8.571428571428 55533.333333333336 64812.5 70700 Male 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 37800 45680 54321.428571428572 64578.947368421053 75671.428571428565 81700
Salary ranges
Average salary
GenderDescriptiveStats
Gender/salary comparison – descriptive statistics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Run descriptive statistics twice – once with named range “malesalary” and again with “femalesalary”, then copy and paste them next to each other | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Column1 | Comparing male and female salary | <== table title centered across columns | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Statistic | <==row headers differentiated from data | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mean | $ 56,466 | $ 50,681 | <==number formatting | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Standard Error | 1474.68 | 54600 | 1515.0288634913 | $ 1,475 | $ 1,515 | <==all statistics that are NOT being used are REMOVED | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Median | 56000 | 49000 | $ 56,000 | $ 49,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mode | 45600 | 39800 | Standard Deviation | $ 12,071 | $ 11,030 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
120 | 0.82 | 11029.5766116484 | Range | $ 48,900 | $ 40,800 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sample | Variance | 145704712.799638 | Sample Variance | 121651560.232221 | Minimum | $ 33,400 | $ 31,200 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kurtosis | -0.8044449928 | -0.9313514963 | Maximum | $ 82,300 | $ 72,000 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Skewness | 0.3 | 0.1 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
48900 | 40800 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
33400 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
31200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
82300 | 72000 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
3783200 | 2686100 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
GenderDescriptiveStats (2)
Table 1 | <== Start with labeleling each table by number, sequentially (charts too - call them "Figure x") | |||
<== table title centered across columns or left justified, meaningful, not abstract | ||||
Malea | <==row headers differentiated from data (bold); lines above and below column headers | |||
<==If you want to show subsets of statistics, use an italicized header, indent following | ||||
Measures of central tendency | <==indented to show part of type of statistic | |||
12070.8207177324 | Measures of central variance | 90% | ||
0.9375 | ||||
0.32 | 0.1887693789 | 0.7162162162 | ||
0.9137387481 | ||||
Test for outliers | ||||
Zscore of Minimum | – | 1.9 | -1.8 | |
Zscore of Maximum | 2.1 | |||
Source: Random sample of 120 RJCorp employees, June 2015 | <==Note: All statistics that are NOT being used are REMOVED | |||
a Notation if needed (superscript used after header “Male” above as an example |
SalaryDistributionHistogram
Salary histogram/distribution | ||||||
Count of Salary | ||||||
30-39 | ||||||
40-49 | ||||||
50-59 | ||||||
60-69 | ||||||
70-79K | ||||||
80-89K |
Histogram of salary
Total 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 18 34 29 27 10 2
Salary levels (in dollars)
Number of employees
Figure 1: Distribution of salaries in RJ Corp
Count of Salary 30-39K 40-49K 50-59K 60-69K 70-79K 80-89K 18 34 29 27 10 2
Salary
Number of employees
GenderDescriptiveStatistics (2
Categorical variable descriptive statistics produced through a pivot table | |||||
Pivot table output | |||||
Count of Gender2 | |||||
44.17% | 12% | ||||
55.83% | |||||
Copy, paste special, paste as a value: | |||||
0.44 | |||||
0.5583333333 | |||||
Format in an attractive manner by standards of good table formatting (see Chapter 9, or PowerPoint) | |||||
Note: I’ve used format as a table from the Home ribbon, the selected “Convert to Range” button to get rid of special drop downs. | |||||
Gender descriptive statistics | <==Title centered across columns or left justified, bold; meaningful | ||||
Percent of | total | <==Column/row headers formatted to distinguish from data, centered | |||
44% | <==Number formatting used - percentage formatting in this case | ||||
56% | |||||
100% | |||||
0.26 |
SalaryDescriptiveStatistics (2
Salary descriptive statistics | ||||||
Table 2 | ||||||
<== table title centered across columns or left justified; meaningful | ||||||
Figures | <==row / column headers differentiated from data | |||||
1088.9229612112 | $ 53,911 | |||||
53100 | $ 53,100 | |||||
48100 | $ 11,929 | |||||
11928.5533848133 | $ 51,100 | |||||
142290385.8543 | ||||||
– | 0.6 | |||||
0.3069 | ||||||
51100 | ||||||
6469300 |
Formatted output from Data Analysis Toolpack,
function
GenderAgeSalary
<20 | $31,200 | |||||||||||||||||
20-29 | $39,000 | $41,060 | ||||||||||||||||
$48,564 | $49,447 | |||||||||||||||||
$54,873 | $54,840 | |||||||||||||||||
$56,638 | $63,914 | |||||||||||||||||
$52,089 | $62,550 | |||||||||||||||||
70-80 | $59,220 |
Comparing gender average salary by age group
Female < 20 20-29 30-39 40-49 50-59 60-69 70-80 31200 39000 48563.63636363636 54872.727272727272 56638.461538461539 52088.888888888891 Male < 20 20-29 30-39 40-49 50-59 60-69 70-80 41060 49446.666666666664 54840 63914.285714285717 62550 59220
Age groups
Average salary
GenderSalaryAvg
$50,681 | -1 | 0.24 | ||||
$56,466 | ||||||
$53,911 | ||||||
-10% | ||||||
Average of Salary Female Male 50681.132075471702 56465.671641791043
AgeAnalysis
Pivot table producing descriptive statistics for chunks of age (age histogram) | |||||||||||||||||
Count of Age | StdDev of Salary | Min of Salary | Max of Salary | ||||||||||||||
ERROR:#DIV/0! | |||||||||||||||||
$39,792 | $4,773 | $33,300 | |||||||||||||||
$49,073 | $7,724 | $34,200 | |||||||||||||||
$54,854 | $8,235 | ||||||||||||||||
$61,132 | $11,434 | $82,300 | |||||||||||||||
$56,273 | $13,295 | ||||||||||||||||
$15,388 | |||||||||||||||||
$11,929 | |||||||||||||||||
coefficient of variation | negative Zscore | positive Zscore | |||||||||||||||
15-24 | $ 35,920 | $ 4,670 | $ 42,100 | $ 10,900 | -1.01 | 1.32 | |||||||||||
25-34 | $ 44,888 | $ 6,832 | $ 34,600 | $ 57,700 | $ 23,100 | 15% | -1.51 | 1.88 | |||||||||
35-44 | $ 51,165 | $ 9,192 | $ 34,200 | $ 75,500 | $ 41,300 | 18% | -1.85 | 2.65 | |||||||||
45-54 | $ 56,926 | $ 9,876 | $ 38,600 | $ 79,000 | $ 40,400 | -1.86 | 2.24 | ||||||||||
55-64 | $ 59,293 | $ 12,956 | $ 39,200 | $ 43,100 | -1.55 | 1.78 | |||||||||||
65-75 | $ 60,411 | $ 13,371 | $ 39,900 | $ 76,000 | $ 36,100 | -1.53 | 1.17 | ||||||||||
Instructions: | |||||||||||||||||
1. Create a pivot table using the numeric variable (age) as the row label | |||||||||||||||||
2. Group the row label – Group button on ribbon. Choose chunks in dialog box. | |||||||||||||||||
3. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item. | |||||||||||||||||
4. To work with data, it is frequently easier to copy pivot table data and paste as – paste as values. |
copy this, paste as values below
Age Line Fit Plot
Salary 19 28 29 26 22 28 38 35 28 27 36 28 36 33 49 38 45 47 30 39 60 47 62 39 53 57 43 61 33 44 51 49 47 53 39 48 49 54 48 50 50 50 51 53 62 57 56 58 60 61 64 66 67 24 20 24 31 27 39 37 35 30 39 37 40 28 42 31 33 59 49 58 54 41 32 50 45 40 56 40 59 56 48 42 38 36 49 49 36 54 36 38 48 47 51 49 51 52 52 49 54 55 56 56 57 57 58 59 59 60 62 63 63 68 69 70 71 72 73 74 31200
39800
48100
50000
00 49000
60000
72000
70000
60000 52300
58000
67500
39800 67500
39600 33400
54100
45600
54600
43500
67500
56700 48100 45000 50000
56700
45600 56300
48100
56000
60000
82300
61000 67800
45600
68000 43200
46500 Predicted Salary 19 28 29 26 22 28 38 35 28 27 36 28 36 33 49 38 45 47 30 39 60 47 62 39 53 57 43 61 33 44 51 49 47 53 39 48 49 54 48 50 50 50 51 53 62 57 56 58 60 61 64 66 67 24 20 24 31 27 39 37 35 30 39 37 40 28 42 31 33 59 49 58 54 41 32 50 45 40 56 40 59 56 48 42 38 36 49 49 36 54 36 38 48 47 51 49 51 52 52 49 54 55 56 56 57 57 58 59 59 60 62 63 63 68 69 70 71 72 73 74 1 Age
Salary
Simply create formulas here referencing values to the left
VariableDescriptiveStatsPHStat
PHStat ouput – Descriptive Statistics for HumanResources.xlsx | ||||||||||||
Descriptive Summary | ||||||||||||
6.47 | 6.62 | 0.56 | ||||||||||
$53,100 | ||||||||||||
$51,100 | ||||||||||||
167.7815 | 15.9485 | 4.5913 | 0.21 | 0.2487 | ||||||||
12.9531 | 3.9936 | 2.14 | 0.4671 | 0.49 | ||||||||
Coeff. of Variation | 22.13% | 27.56% | 61.76% | 32. | 38% | 147.5 | 1% | 89. | 31% | |||
-0.0986 | 0.8545 | 0.1834 | 0.7982 | -0.2379 | ||||||||
-0.6662 | -0.7283 | 0.0532 | -0.5082 | -1.3862 | -1.9766 | |||||||
1088.9230 | 1.1824 | 0.3646 | 0.1956 | 0.0426 | 0.0455 | |||||||
Descriptive statistics summary | ||||||||||||
Students should get rid of anything that is not covered in the course and they don’t understand in the output.
Tables should have headers differentiated, number formatting done, centered data.
GenderAnalysis
Analysis of varibles in terms of gender via pivot table | |||||||||||||||||
Average of Age | Average of YrsWork | Average of JGClass | Average of EthnicityCODE | ||||||||||||||
$11,030 | 45.3 | 6.0 | |||||||||||||||
$12,071 | 48.3 | 7.4 | 7.1 | ||||||||||||||
47.0 | 6.5 | ||||||||||||||||
1. Create a pivot table using the categorical variable (gender) as the row label | |||||||||||||||||
2. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item. | |||||||||||||||||
3. To work with data, it is frequently easier to copy pivot table data and paste as – paste as values. | |||||||||||||||||
Endogenous variables | Other independent variables | ||||||||||||||||
5.98 | 7.12 | 45.34 | 48.31 | 5.30 | 7.39 | 0.40 | |||||||||||
0.27 | 1.72 | 1.6 | 0.45 | 0.53 | 0.06 | ||||||||||||
$ 39,800 | $ 45,600 | ||||||||||||||||
1.99 | 12.55 | 3.24 | 4.3 | 0.41 | |||||||||||||
Coefficient of variance | 21% | 30% | 28% | 27% | 61% | 58% | 197% | 123% | |||||||||
Zscore negative | $ (1.77) | $ (1.91) | -1.50 | -1.92 | -2.10 | -2.14 | -1.33 | -1.48 | – | 0.51 | -0.82 | ||||||
Zscore positive | $ 1.93 | $ 2.14 | 2.53 | 1.81 | 1.73 | 1.94 | 2.99 | 2.47 | 1.21 | ||||||||
Quartile 1 | $ 46,250 | ||||||||||||||||
Quartile 3 | $ 58,000 | $ 65,250 | |||||||||||||||
Inter Quartile Range | $ 17,600 | $ 19,000 | |||||||||||||||
Note: I created special named ranges in the data to make it easier – e.g., SalaryFemale, SalaryMale |
SalaryPivotTable
Analysis of variables in terms of chunks of salary | |||||
Average of Gender code | |||||
38.17 | 0.28 | 4.22 | 4.00 | ||
43.65 | 0.38 | 0.59 | 5.59 | ||
44.93 | 0.48 | 6.45 | |||
56.19 | 0.33 | 0.70 | 8.00 | 8.33 | |
53.30 | 0.10 | 10.30 | 9.40 | ||
80000-89999 | 58.00 | 1.00 | 15.00 | 11.00 | |
0.3166666667 | 6.4666666667 | 6.61 |
GenderCompareDescriptives
Table comparing descriptve statistics for all variables in terms of gender | |||||||||||
2.0 | |||||||||||
157.3824383164 | 174.5517865219 | 10.5224963716 | 18.513794663 | 3.9419448476 | 4.591587517 | 0.1676342525 | 0.2442333786 | ||||
-0.92511818 | -0.6818428647 | 1.0936677151 | -0.4368448489 | -0.4548349394 | -0.5676589436 | 0.2105423988 | -1.8936805556 | ||||
-0.2357663046 | -0.0428389974 | 1.1727443433 | 0.5747426633 | 0.2109272442 | 0.1068805146 | 1.4846023258 | 0.4046946723 | ||||
2403 | 3237 | 281 | 495 | ||||||||
48.3134328358 | 7.3880597015 | 7.1194029851 | 0.4029850746 | ||||||||
1.6140788534 | 0.5256665231 | 0.2617845621 | 0.0603761071 | ||||||||
13.211804817 | 4.3027659317 | 2.1427989913 | 0.4941997355 | ||||||||
PivotTableCreatePercentPolygon
Pivot table used to create percent polygon – comparing percents of males vs. females in terms of chunks of age | ||||||
Count of Age2 | ||||||
3.77% | 4.48% | |||||
15.09% | 18.87% | 10.45% | ||||
20.75% | 23.88% | |||||
33.96% | 25.37% | |||||
24.53% | ||||||
7.46% | ||||||
31.34% | ||||||
8.96% | ||||||
1. Pivot table created using gender and then age as row labels | ||||||
2. Group age row labels | ||||||
3. Create a count column (not necessary) | ||||||
4. Drag age again to the values box. | ||||||
5. Chage values – click Show Values As, choose Percent Of Parent Row Total | ||||||
6. Copy data, paste as values, then create a line chart with that | ||||||
– you will have to check the row labels – if there are no values in a chunk, Excel will not show the chunk. Simply type it in manually and insert a value of zero. | ||||||
Comparing counts of gender by bins of age | ||||||
20-34 | ||||||
35-49 | ||||||
50-64 | ||||||
6 | 5-8 |
Female 15-24 25-34 35-44 45-54 55-64 65-75 3.7735849056603772E-2 0.18867924528301888 0.18867924528301888 0.33962264150943394 0.20754716981132076 3.7735849056603772E-2 Male 15-24 25-34 35-44 45-54 55-64 65-75 4.4776119402985072E-2 0.1044776119402985
4925373 0.2537313432835821 0.2537313432835821 0.1044776119402985 Female 15-24 25-34 35-44 45-54 55-64 65-75 3.7735849056603772E-2 0.18867924528301888 0.18867924528301888 0.33962264150943394 0.20754716981132076 3.7735849056603772E-2 Male 15-24 25-34 35-44 45-54 55-64 65-75 4.4776119402985072E-2 0.1044776119402985 0.23880597014925373 0.2537313432835821 0.2537313432835821 0.1044776119402985
Comparing gender by age
male < 20 20-34 35-49 50-64
0 10 25 25 7 female < 20 20-34 35-49 50-64 65-80 1 11 19 20 2
PercentPolygonGenderYearsWorked
Compare distributions of male vs. female in terms of years worked | ||||||||||
Count of YrsWork | Count of YrsWork2 | |||||||||
1-3 | 0.2089552239 | 0.4150943396 | ||||||||
1-4 | 47.17% | 4-6 | 0.320754717 | |||||||
37.74% | 7-9 | 0.3134328358 | 0.1509433962 | |||||||
9-12 | 11. | 32% | 10-12 | 0.0895522388 | 0.0754716981 | |||||
13-16 | 13-15 | 0.1194029851 | 0.0377358491 | |||||||
16-18 | 0.0298507463 | |||||||||
29.85% | ||||||||||
13.43% | ||||||||||
17-20 | 1.49% | |||||||||
47% | ||||||||||
24% | 11% | |||||||||
Comparing percents in years worked by gender
Male 1-3 4-6 7-9 10-12 13-15 16-18 0.20895522388059701 0.23880597014925373 0.31343283582089554 8.9552238805970144E-2 0.11940298507462686 2.9850746268656716E-2 Female 1-3 4-6 7-9 10-12 13-15 16-18 0.41509433962264153 0.32075471698113206 0.15094339622641509 7.5471698113207544E-2 3.7735849056603772E-2 0
% Of Each Age Category per Age Grouping
Male 1-3 4-6 7-9 10-12 13-15 16-18 0.20895522388059701 0.23880597014925373 0.31343283582089554 8.9552238805970144E-2 0.11940 298507462686 2.9850746268656716E-2 Female 1-3 4-6 7-9 10-12 13-15 16-18 0.41509433962264153 0.32075471698113206 0.15094339622641509 7.5471698113207544E-2 3.7735849056603772E-2 0 Years worked
Overall percentage
Comparing counts in years worked by gender
male 1-4 5-8 9-12 13-16 17-20 20 21 16 9 1 female 1-4 5-8 9-12 13-16 17-20 25 20 6 2 0
EthnicitySalaryAnalysis
Ethnicity and salary | ||||||||||
Count of Ethnicity | StdDev of Salary2 | Min of Salary2 | ||||||||
$50,097 | $11,216 | |||||||||
$55,678 | $11,899 | |||||||||
$35,600 | ||||||||||
Non Minority | ||||||||||
-1.6873 | -1.6849 | |||||||||
2.2372 | 1.9706 | |||||||||
$41,000 |
OptionalEthnicitySalaryAnalysis
Copy, Paste Values below: | |||||
Optional Ethnicity Salary Analiysis – percent polygon | Note: For minority, a row label is missing because there is no data, | ||||
you need to manually add that and input a value of zero | |||||
Count of Ethnicity2 | Non-Minority | ||||
21.05% | 12. | 20% | 26% | ||
34.21% | 25.61% | ||||
18.42% | 26.83% | ||||
23.68% | 21.95% | ||||
2.63% | 10.98% | ||||
2.44% | |||||
Non-minority | |||||
Comparing % of non/minority by bins of salary
Minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 0.21052631578947367
210526315789475 0.18421052631578946 0.23684210526315788 2.6315789473684209E-2 0 Non-Minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 0.12195121951219512 0.25609756097560976 0.26829268292682928 0.21951219512195122 0.10975609756097561 2.4390243902439025E-2
Comparing non/minority counts by bins of salary
Minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 8 13 7 9 1 0 Non-minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 10 21 22 18 9 2
EthnicityJGClassAnalysis
Count of JGClass | Average of JGClass4 | StdDev of JGClass3 | Min of JGClass2 | Max of JGClass | ||||
6.6842105263 | 2.1573916077 | |||||||
6.5853658537 | 2.1485063403 | |||||||
6.6166666667 | 2.1427357575 | |||||||
-1.6688 | 2.0547 | |||||||
YearsWorkedSalaryAnalysis
YearsWorkedSalaryAnalysis!A1 | ||||||
Years worked and salary | ||||||
Average of Salary4 | ||||||
$ 48,476 | $ 9,496 | 0.29 | ||||
$ 54,300 | $ 10,909 | $ 76,500 | ||||
$ 58,150 | $ 11,703 | $ 77,500 | $ 43,200 | 7.36 | 0.73 | |
$ 64,800 | $ 14,319 | 8.82 | ||||
$ 69,500 | 9.00 | |||||
Average of Salary3 | ||||||
48475.5555555556 | 9496.0794674711 | |||||
54300 | 10908.8954527945 | |||||
58150 | 11702.6553763299 | |||||
64800 | 14319.1480193481 | |||||
AgeSalaryAnalysis
Age/Salary Analysis | |||||||
Age and Salary | Age and other variables | ||||||
StdDev of Salary3 | Max of Salary2 | ||||||
$ 39,792 | $ 4,773 | $ 48,100 | $ 33,300 | 3.0769230769 | 0.3846153846 | 4.3846153846 | |
$ 49,073 | $ 7,724 | $ 70,000 | 5.7307692308 | 0.5769230769 | 5.8461538462 | ||
$ 54,854 | $ 8,235 | 5.3076923077 | 6.6153846154 | ||||
$ 61,132 | $ 11,434 | $ 38,700 | 7.7647058824 | 0.6176470588 | 7.7058823529 | ||
$ 56,273 | $ 13,295 | 8.2666666667 | |||||
$ 59,220 | $ 15,388 | ||||||
AgeJobGradeClassAnalysis
Age and Job Grade Classification analysis | |||||
Average of JGClass5 | StdDev of JGClass4 | Max of JGClass3 | |||
1.2608503439 | |||||
1.7132964178 | |||||
1.6988684017 | |||||
2.0820941056 | |||||
2.2928460169 | |||||
2.19089023 | |||||
4.7142857143 | 1.45405836 | ||||
6.4090909091 | 1.702504063 | ||||
7.5777777778 | 2.1583757455 | ||||
7.6666666667 | |||||
DataCopy
47700 |
Cross-Class-Table
Cross Classification Table |
Summary Table
One-Way Summary Table |
Bar Chart
Bar Chart
Total Minority Not Minority 38 82 Ethnicity
Histogram
TableOfContents!A1
Freq. & % Distribution
Frequency Distribution for Salary | ||
midpts | Percentage | |
29999.9 | 0.0% | |
39999.9 | 35000 | 15.0% |
49999.9 | 2 | 8.3% |
59999.9 | 55000 | 24.2% |
69999.9 | 65000 | 22.5% |
79999.9 | 75000 | |
89999.9 | 85000 | 1.7% |
100.0% |
% Polygons 2 Groups
TableOfContents!A1
SideBySide Bar Chart
Side-By-Side Chart
Female Minority Not Minority 11 42 Male Minority Not Minority 27 40
>TableOfContents
with hyperlinks for this document
l data
2
Table of contents
Excluding standard worksheets that come with the origi
na
Sheet name
Purpose
NotesOn
Prep
!A
for students in doing data analysis in Excel
PivotTable!A1
!A1
VariableDescriptiveStatsPHStat
!A1
Regression
!A1
PivotTableCreate
Percent
Polygon
!A1
univariate descriptive statistics
GenderAnalysis
!A1
GenderCompareDescriptives
!A1
SalaryAnalysis!A1
Optional
EthnicitySalaryAnalysis
!A1
Ethnicity
JGClass
Analysis
!A1
AgeSalaryAnalysis
!A1
AgeJobGradeClassAnalysis
!A1
YearsWorkedSalaryAnalysis
!A1
/Salary analysis
PercentPolygonGenderYearsWorked
!A1
distribution by gender; Example of comparing distributions between two categories with different number of cases or different scales, i.e., version of percent polygon
Variable INFO
‘!A1
Human Resources DATA
‘!A1 Data
Cross-Class-Table
‘!A1
Sum
mary Table
‘!A1
Histogram
!A1
% Polygons 2 Groups
‘!A1
Freq. & % Distribution
‘!A1
Variable INFO
TableOfContents!A1 |
responses to a survey conducted by the VP of Human Resources at a large company.
01 class at Montclair State University
in years
, 5,
,
,
(lowest skill job to highest skill job)
,
)
s created in this worksheet – use these names to address the data more quickly then manually selecting data
1.
75
7
465.
1
91
male salary
%
Human Resources DATA
EthnicityCODE | Gender code | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 31 | 19 | Female | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 40 | 28 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 42 | 60 | 29 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 39 | 26 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 33 | 30 | 22 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 35 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 34 | 38 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 43 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 37 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$34,600 | 27 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$37, | 70 | 36 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 48 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$38,900 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 58 | 49 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 52 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,500 | 45 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$52,300 | 47 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$50,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 54 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 57 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,700 | 62 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$49,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$70,100 | 53 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$60,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$48,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$57,000 | 61 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$57,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,800 | 44 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,600 | 51 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 59 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 72 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$43,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$70,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$54,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 55 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$67,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
15 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$38,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$62,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$65,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$43,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$67,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$56,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$39,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$39,200 | 14 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$58,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$39,800 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
66 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 68 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
24 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$33,400 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$42,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$56,300 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$45,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$48,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$54,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$50,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,800 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$44,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$56,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$37,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$45,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$45,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$62,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
41 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$48,100 | 32 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$75,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$66,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$62,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$47,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$53,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$54,900 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$53,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$43,300 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$46,400 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$64,300 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$61,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$38,600 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$56,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$60,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$64,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$52,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$79,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$76,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$62,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$72,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$61,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$68,700 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 82 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$67,800 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$81,100 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
16 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$77,500 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$68,000 | 63 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$ | 73 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
69 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$76,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$69,500 | 18 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$39,900 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
$64,200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
74 |
NotesOnDataPrep
1. It will make the student’s life easier to create named ranges in the data for the ranges they need. Simply sort, highlight the range, and in the box upper left, type in a name. Use that name in functions and formulas (e.g., quartile(), or descriptive stats – you can use named ranges in PHStat and Data Analysis Toolpack) | ||
2. Note that Pivot tables can provide all descriptive statistics except median, quartiles, IQR. If Zscores indicate that there is an outlier on one side, students should not be using the mean, but as a work around, you can ask them to note that, discuss what it means and then use the mean/SD anyway; OR you can require them to manually create those separately from the pivot table (or don’t use a pivot table, use the data analysis toolpack or PHSTat). | ||
3. | Instructions | |
a. Create a pivot table using the numeric variable (age) as the row label | ||
b. Group the row label – Group button on ribbon. Choose chunks in dialog box. | ||
make sure you click in the data, not the header, or the button will be greyed out | ||
Play with the beginning, end value and chunks to make | bins | |
c. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item. | ||
d. To work with data, it is frequently easier to copy pivot table data and paste as – paste as values. | ||
e. Word of warning: If you divide data into subcategories – chunks of salary for women, men – if there are no values for a category, Excel won’t list it – you have to manually put a zero in for the value. | ||
4. Getting Excel stuff into Word for a report: It might be easier to paste as a picture object – easier to manipulate. |
PercentDifference
Count | Column Labels | ||||||||||||||||||||||||||||||||||||||||||||||||||
Row Labels | Grand | Total | |||||||||||||||||||||||||||||||||||||||||||||||||
16.98% | 5.97% | 10.8 | 3% | ||||||||||||||||||||||||||||||||||||||||||||||||
33.9 | 6% | 2 | 5.3 | 29. | 17% | ||||||||||||||||||||||||||||||||||||||||||||||
96% | 35.8 | 2% | 35.0 | 0% | |||||||||||||||||||||||||||||||||||||||||||||||
1 | 3.2 | 22.39% | 18. | 33% | |||||||||||||||||||||||||||||||||||||||||||||||
1.89% | 1 | 0.4 | 5% | 6.6 | |||||||||||||||||||||||||||||||||||||||||||||||
Grand Total | 10 | 0.00 | 10 | 0.00% | Count of Gender | ||||||||||||||||||||||||||||||||||||||||||||||
3 | 4% | 25 | 29% | ||||||||||||||||||||||||||||||||||||||||||||||||
34% | 36% | ||||||||||||||||||||||||||||||||||||||||||||||||||
13% | 22% | 52% | |||||||||||||||||||||||||||||||||||||||||||||||||
10% | 139% |
Percent difference in Male to
Proportions
3 5 7 9 11 0.95950920245398763
8951115329852861 5.326876513
1914E-2
1582278481012667 1.3881278538812787 3 5 7 9 11 3 5 7 9 11
3 5 7 9 11 1
Job Levels
LevelGender
Average of Salary | ||||||||||
38484.6153846154 | ||||||||||
3755 | 5.55 | |||||||||
40575 | ||||||||||
4634 | 5.71 | |||||||||
45250 | ||||||||||
47505.8823529412 | ||||||||||
54809.5238095238 | ||||||||||
57194.4444444444 | ||||||||||
53020.8333333333 | ||||||||||
65740.9090909091 | ||||||||||
62371.4285714286 | ||||||||||
67313.3333333333 | ||||||||||
74825 | ||||||||||
67500 | ||||||||||
75871.4285714286 | ||||||||||
53910.8333333333 |
Average salary
of gender on each level
Total
Female Male Female Male Female Male Female Male Female Male 3 5 7 9 11 37555.555555555555 40575 45250 47505.882352941175 57194.444444444445 53020.833333333336 62371.428571428572 67313.333333333328 67500 75871.428571428565
BiVariateDistributionChart
30000-39999 | 36938.4615384615 | 37800 | 37177.7777777778 | ||||||||||||||||
40000-49999 | 45878.5714285714 | 45680 | 4576 | 1.7 | |||||||||||||||
50000 | 55533.3333333333 | 543 | 21 | 54948.275862069 | |||||||||||||||
60000 | 648 | 12.5 | 64578.9473684211 | 64648.1481481481 | |||||||||||||||
70000 | 70700 | 75671.4285714286 | 74180 | ||||||||||||||||
80000-90000 | 81700 | ||||||||||||||||||
50681.1320754717 | 5646 | 5.67 |
Gender average salary comparison by salary level
Female 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 36938.461538461539 4587 8.571428571428 55533.333333333336 64812.5 70700 Male 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 37800 45680 54321.428571428572 64578.947368421053 75671.428571428565 81700
Salary ranges
Average salary
GenderDescriptiveStats
Gender/salary comparison – descriptive statistics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Run descriptive statistics twice – once with named range “malesalary” and again with “femalesalary”, then copy and paste them next to each other | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Column1 | Comparing male and female salary | <== table title centered across columns | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Statistic | <==row headers differentiated from data | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mean | $ 56,466 | $ 50,681 | <==number formatting | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Standard Error | 1474.68 | 54600 | 1515.0288634913 | $ 1,475 | $ 1,515 | <==all statistics that are NOT being used are REMOVED | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Median | 56000 | 49000 | $ 56,000 | $ 49,000 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mode | 45600 | 39800 | Standard Deviation | $ 12,071 | $ 11,030 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
120 | 0.82 | 11029.5766116484 | Range | $ 48,900 | $ 40,800 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sample | Variance | 145704712.799638 | Sample Variance | 121651560.232221 | Minimum | $ 33,400 | $ 31,200 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kurtosis | -0.8044449928 | -0.9313514963 | Maximum | $ 82,300 | $ 72,000 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Skewness | 0.3 | 0.1 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
48900 | 40800 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
33400 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
31200 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
82300 | 72000 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
3783200 | 2686100 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
GenderDescriptiveStats (2)
Table 1 | <== Start with labeleling each table by number, sequentially (charts too - call them "Figure x") | |||
<== table title centered across columns or left justified, meaningful, not abstract | ||||
Malea | <==row headers differentiated from data (bold); lines above and below column headers | |||
<==If you want to show subsets of statistics, use an italicized header, indent following | ||||
Measures of central tendency | <==indented to show part of type of statistic | |||
12070.8207177324 | Measures of central variance | 90% | ||
0.9375 | ||||
0.32 | 0.1887693789 | 0.7162162162 | ||
0.9137387481 | ||||
Test for outliers | ||||
Zscore of Minimum | – | 1.9 | -1.8 | |
Zscore of Maximum | 2.1 | |||
Source: Random sample of 120 RJCorp employees, June 2015 | <==Note: All statistics that are NOT being used are REMOVED | |||
a Notation if needed (superscript used after header “Male” above as an example |
SalaryDistributionHistogram
Salary histogram/distribution | ||||||
Count of Salary | ||||||
30-39 | ||||||
40-49 | ||||||
50-59 | ||||||
60-69 | ||||||
70-79K | ||||||
80-89K |
Histogram of salary
Total 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 18 34 29 27 10 2
Salary levels (in dollars)
Number of employees
Figure 1: Distribution of salaries in RJ Corp
Count of Salary 30-39K 40-49K 50-59K 60-69K 70-79K 80-89K 18 34 29 27 10 2
Salary
Number of employees
GenderDescriptiveStatistics (2
Categorical variable descriptive statistics produced through a pivot table | |||||
Pivot table output | |||||
Count of Gender2 | |||||
44.17% | 12% | ||||
55.83% | |||||
Copy, paste special, paste as a value: | |||||
0.44 | |||||
0.5583333333 | |||||
Format in an attractive manner by standards of good table formatting (see Chapter 9, or PowerPoint) | |||||
Note: I’ve used format as a table from the Home ribbon, the selected “Convert to Range” button to get rid of special drop downs. | |||||
Gender descriptive statistics | <==Title centered across columns or left justified, bold; meaningful | ||||
Percent of | total | <==Column/row headers formatted to distinguish from data, centered | |||
44% | <==Number formatting used - percentage formatting in this case | ||||
56% | |||||
100% | |||||
0.26 |
SalaryDescriptiveStatistics (2
Salary descriptive statistics | ||||||
Table 2 | ||||||
<== table title centered across columns or left justified; meaningful | ||||||
Figures | <==row / column headers differentiated from data | |||||
1088.9229612112 | $ 53,911 | |||||
53100 | $ 53,100 | |||||
48100 | $ 11,929 | |||||
11928.5533848133 | $ 51,100 | |||||
142290385.8543 | ||||||
– | 0.6 | |||||
0.3069 | ||||||
51100 | ||||||
6469300 |
Formatted output from Data Analysis Toolpack,
function
GenderAgeSalary
<20 | $31,200 | |||||||||||||||||
20-29 | $39,000 | $41,060 | ||||||||||||||||
$48,564 | $49,447 | |||||||||||||||||
$54,873 | $54,840 | |||||||||||||||||
$56,638 | $63,914 | |||||||||||||||||
$52,089 | $62,550 | |||||||||||||||||
70-80 | $59,220 |
Comparing gender average salary by age group
Female < 20 20-29 30-39 40-49 50-59 60-69 70-80 31200 39000 48563.63636363636 54872.727272727272 56638.461538461539 52088.888888888891 Male < 20 20-29 30-39 40-49 50-59 60-69 70-80 41060 49446.666666666664 54840 63914.285714285717 62550 59220
Age groups
Average salary
GenderSalaryAvg
$50,681 | -1 | 0.24 | ||||
$56,466 | ||||||
$53,911 | ||||||
-10% | ||||||
Average of Salary Female Male 50681.132075471702 56465.671641791043
AgeAnalysis
Pivot table producing descriptive statistics for chunks of age (age histogram) | |||||||||||||||||
Count of Age | StdDev of Salary | Min of Salary | Max of Salary | ||||||||||||||
ERROR:#DIV/0! | |||||||||||||||||
$39,792 | $4,773 | $33,300 | |||||||||||||||
$49,073 | $7,724 | $34,200 | |||||||||||||||
$54,854 | $8,235 | ||||||||||||||||
$61,132 | $11,434 | $82,300 | |||||||||||||||
$56,273 | $13,295 | ||||||||||||||||
$15,388 | |||||||||||||||||
$11,929 | |||||||||||||||||
coefficient of variation | negative Zscore | positive Zscore | |||||||||||||||
15-24 | $ 35,920 | $ 4,670 | $ 42,100 | $ 10,900 | -1.01 | 1.32 | |||||||||||
25-34 | $ 44,888 | $ 6,832 | $ 34,600 | $ 57,700 | $ 23,100 | 15% | -1.51 | 1.88 | |||||||||
35-44 | $ 51,165 | $ 9,192 | $ 34,200 | $ 75,500 | $ 41,300 | 18% | -1.85 | 2.65 | |||||||||
45-54 | $ 56,926 | $ 9,876 | $ 38,600 | $ 79,000 | $ 40,400 | -1.86 | 2.24 | ||||||||||
55-64 | $ 59,293 | $ 12,956 | $ 39,200 | $ 43,100 | -1.55 | 1.78 | |||||||||||
65-75 | $ 60,411 | $ 13,371 | $ 39,900 | $ 76,000 | $ 36,100 | -1.53 | 1.17 | ||||||||||
Instructions: | |||||||||||||||||
1. Create a pivot table using the numeric variable (age) as the row label | |||||||||||||||||
2. Group the row label – Group button on ribbon. Choose chunks in dialog box. | |||||||||||||||||
3. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item. | |||||||||||||||||
4. To work with data, it is frequently easier to copy pivot table data and paste as – paste as values. |
copy this, paste as values below
Age Line Fit Plot
Salary 19 28 29 26 22 28 38 35 28 27 36 28 36 33 49 38 45 47 30 39 60 47 62 39 53 57 43 61 33 44 51 49 47 53 39 48 49 54 48 50 50 50 51 53 62 57 56 58 60 61 64 66 67 24 20 24 31 27 39 37 35 30 39 37 40 28 42 31 33 59 49 58 54 41 32 50 45 40 56 40 59 56 48 42 38 36 49 49 36 54 36 38 48 47 51 49 51 52 52 49 54 55 56 56 57 57 58 59 59 60 62 63 63 68 69 70 71 72 73 74 31200
39800
48100
50000
00 49000
60000
72000
70000
60000 52300
58000
67500
39800 67500
39600 33400
54100
45600
54600
43500
67500
56700 48100 45000 50000
56700
45600 56300
48100
56000
60000
82300
61000 67800
45600
68000 43200
46500 Predicted Salary 19 28 29 26 22 28 38 35 28 27 36 28 36 33 49 38 45 47 30 39 60 47 62 39 53 57 43 61 33 44 51 49 47 53 39 48 49 54 48 50 50 50 51 53 62 57 56 58 60 61 64 66 67 24 20 24 31 27 39 37 35 30 39 37 40 28 42 31 33 59 49 58 54 41 32 50 45 40 56 40 59 56 48 42 38 36 49 49 36 54 36 38 48 47 51 49 51 52 52 49 54 55 56 56 57 57 58 59 59 60 62 63 63 68 69 70 71 72 73 74 1 Age
Salary
Simply create formulas here referencing values to the left
VariableDescriptiveStatsPHStat
PHStat ouput – Descriptive Statistics for HumanResources.xlsx | ||||||||||||
Descriptive Summary | ||||||||||||
6.47 | 6.62 | 0.56 | ||||||||||
$53,100 | ||||||||||||
$51,100 | ||||||||||||
167.7815 | 15.9485 | 4.5913 | 0.21 | 0.2487 | ||||||||
12.9531 | 3.9936 | 2.14 | 0.4671 | 0.49 | ||||||||
Coeff. of Variation | 22.13% | 27.56% | 61.76% | 32. | 38% | 147.5 | 1% | 89. | 31% | |||
-0.0986 | 0.8545 | 0.1834 | 0.7982 | -0.2379 | ||||||||
-0.6662 | -0.7283 | 0.0532 | -0.5082 | -1.3862 | -1.9766 | |||||||
1088.9230 | 1.1824 | 0.3646 | 0.1956 | 0.0426 | 0.0455 | |||||||
Descriptive statistics summary | ||||||||||||
Students should get rid of anything that is not covered in the course and they don’t understand in the output.
Tables should have headers differentiated, number formatting done, centered data.
GenderAnalysis
Analysis of varibles in terms of gender via pivot table | |||||||||||||||||
Average of Age | Average of YrsWork | Average of JGClass | Average of EthnicityCODE | ||||||||||||||
$11,030 | 45.3 | 6.0 | |||||||||||||||
$12,071 | 48.3 | 7.4 | 7.1 | ||||||||||||||
47.0 | 6.5 | ||||||||||||||||
1. Create a pivot table using the categorical variable (gender) as the row label | |||||||||||||||||
2. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item. | |||||||||||||||||
3. To work with data, it is frequently easier to copy pivot table data and paste as – paste as values. | |||||||||||||||||
Endogenous variables | Other independent variables | ||||||||||||||||
5.98 | 7.12 | 45.34 | 48.31 | 5.30 | 7.39 | 0.40 | |||||||||||
0.27 | 1.72 | 1.6 | 0.45 | 0.53 | 0.06 | ||||||||||||
$ 39,800 | $ 45,600 | ||||||||||||||||
1.99 | 12.55 | 3.24 | 4.3 | 0.41 | |||||||||||||
Coefficient of variance | 21% | 30% | 28% | 27% | 61% | 58% | 197% | 123% | |||||||||
Zscore negative | $ (1.77) | $ (1.91) | -1.50 | -1.92 | -2.10 | -2.14 | -1.33 | -1.48 | – | 0.51 | -0.82 | ||||||
Zscore positive | $ 1.93 | $ 2.14 | 2.53 | 1.81 | 1.73 | 1.94 | 2.99 | 2.47 | 1.21 | ||||||||
Quartile 1 | $ 46,250 | ||||||||||||||||
Quartile 3 | $ 58,000 | $ 65,250 | |||||||||||||||
Inter Quartile Range | $ 17,600 | $ 19,000 | |||||||||||||||
Note: I created special named ranges in the data to make it easier – e.g., SalaryFemale, SalaryMale |
SalaryPivotTable
Analysis of variables in terms of chunks of salary | |||||
Average of Gender code | |||||
38.17 | 0.28 | 4.22 | 4.00 | ||
43.65 | 0.38 | 0.59 | 5.59 | ||
44.93 | 0.48 | 6.45 | |||
56.19 | 0.33 | 0.70 | 8.00 | 8.33 | |
53.30 | 0.10 | 10.30 | 9.40 | ||
80000-89999 | 58.00 | 1.00 | 15.00 | 11.00 | |
0.3166666667 | 6.4666666667 | 6.61 |
GenderCompareDescriptives
Table comparing descriptve statistics for all variables in terms of gender | |||||||||||
2.0 | |||||||||||
157.3824383164 | 174.5517865219 | 10.5224963716 | 18.513794663 | 3.9419448476 | 4.591587517 | 0.1676342525 | 0.2442333786 | ||||
-0.92511818 | -0.6818428647 | 1.0936677151 | -0.4368448489 | -0.4548349394 | -0.5676589436 | 0.2105423988 | -1.8936805556 | ||||
-0.2357663046 | -0.0428389974 | 1.1727443433 | 0.5747426633 | 0.2109272442 | 0.1068805146 | 1.4846023258 | 0.4046946723 | ||||
2403 | 3237 | 281 | 495 | ||||||||
48.3134328358 | 7.3880597015 | 7.1194029851 | 0.4029850746 | ||||||||
1.6140788534 | 0.5256665231 | 0.2617845621 | 0.0603761071 | ||||||||
13.211804817 | 4.3027659317 | 2.1427989913 | 0.4941997355 | ||||||||
PivotTableCreatePercentPolygon
Pivot table used to create percent polygon – comparing percents of males vs. females in terms of chunks of age | ||||||
Count of Age2 | ||||||
3.77% | 4.48% | |||||
15.09% | 18.87% | 10.45% | ||||
20.75% | 23.88% | |||||
33.96% | 25.37% | |||||
24.53% | ||||||
7.46% | ||||||
31.34% | ||||||
8.96% | ||||||
1. Pivot table created using gender and then age as row labels | ||||||
2. Group age row labels | ||||||
3. Create a count column (not necessary) | ||||||
4. Drag age again to the values box. | ||||||
5. Chage values – click Show Values As, choose Percent Of Parent Row Total | ||||||
6. Copy data, paste as values, then create a line chart with that | ||||||
– you will have to check the row labels – if there are no values in a chunk, Excel will not show the chunk. Simply type it in manually and insert a value of zero. | ||||||
Comparing counts of gender by bins of age | ||||||
20-34 | ||||||
35-49 | ||||||
50-64 | ||||||
6 | 5-8 |
Female 15-24 25-34 35-44 45-54 55-64 65-75 3.7735849056603772E-2 0.18867924528301888 0.18867924528301888 0.33962264150943394 0.20754716981132076 3.7735849056603772E-2 Male 15-24 25-34 35-44 45-54 55-64 65-75 4.4776119402985072E-2 0.1044776119402985
4925373 0.2537313432835821 0.2537313432835821 0.1044776119402985 Female 15-24 25-34 35-44 45-54 55-64 65-75 3.7735849056603772E-2 0.18867924528301888 0.18867924528301888 0.33962264150943394 0.20754716981132076 3.7735849056603772E-2 Male 15-24 25-34 35-44 45-54 55-64 65-75 4.4776119402985072E-2 0.1044776119402985 0.23880597014925373 0.2537313432835821 0.2537313432835821 0.1044776119402985
Comparing gender by age
male < 20 20-34 35-49 50-64
0 10 25 25 7 female < 20 20-34 35-49 50-64 65-80 1 11 19 20 2
PercentPolygonGenderYearsWorked
Compare distributions of male vs. female in terms of years worked | ||||||||||
Count of YrsWork | Count of YrsWork2 | |||||||||
1-3 | 0.2089552239 | 0.4150943396 | ||||||||
1-4 | 47.17% | 4-6 | 0.320754717 | |||||||
37.74% | 7-9 | 0.3134328358 | 0.1509433962 | |||||||
9-12 | 11. | 32% | 10-12 | 0.0895522388 | 0.0754716981 | |||||
13-16 | 13-15 | 0.1194029851 | 0.0377358491 | |||||||
16-18 | 0.0298507463 | |||||||||
29.85% | ||||||||||
13.43% | ||||||||||
17-20 | 1.49% | |||||||||
47% | ||||||||||
24% | 11% | |||||||||
Comparing percents in years worked by gender
Male 1-3 4-6 7-9 10-12 13-15 16-18 0.20895522388059701 0.23880597014925373 0.31343283582089554 8.9552238805970144E-2 0.11940298507462686 2.9850746268656716E-2 Female 1-3 4-6 7-9 10-12 13-15 16-18 0.41509433962264153 0.32075471698113206 0.15094339622641509 7.5471698113207544E-2 3.7735849056603772E-2 0
% Of Each Age Category per Age Grouping
Male 1-3 4-6 7-9 10-12 13-15 16-18 0.20895522388059701 0.23880597014925373 0.31343283582089554 8.9552238805970144E-2 0.11940 298507462686 2.9850746268656716E-2 Female 1-3 4-6 7-9 10-12 13-15 16-18 0.41509433962264153 0.32075471698113206 0.15094339622641509 7.5471698113207544E-2 3.7735849056603772E-2 0 Years worked
Overall percentage
Comparing counts in years worked by gender
male 1-4 5-8 9-12 13-16 17-20 20 21 16 9 1 female 1-4 5-8 9-12 13-16 17-20 25 20 6 2 0
EthnicitySalaryAnalysis
Ethnicity and salary | ||||||||||
Count of Ethnicity | StdDev of Salary2 | Min of Salary2 | ||||||||
$50,097 | $11,216 | |||||||||
$55,678 | $11,899 | |||||||||
$35,600 | ||||||||||
Non Minority | ||||||||||
-1.6873 | -1.6849 | |||||||||
2.2372 | 1.9706 | |||||||||
$41,000 |
OptionalEthnicitySalaryAnalysis
Copy, Paste Values below: | |||||
Optional Ethnicity Salary Analiysis – percent polygon | Note: For minority, a row label is missing because there is no data, | ||||
you need to manually add that and input a value of zero | |||||
Count of Ethnicity2 | Non-Minority | ||||
21.05% | 12. | 20% | 26% | ||
34.21% | 25.61% | ||||
18.42% | 26.83% | ||||
23.68% | 21.95% | ||||
2.63% | 10.98% | ||||
2.44% | |||||
Non-minority | |||||
Comparing % of non/minority by bins of salary
Minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 0.21052631578947367
210526315789475 0.18421052631578946 0.23684210526315788 2.6315789473684209E-2 0 Non-Minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 0.12195121951219512 0.25609756097560976 0.26829268292682928 0.21951219512195122 0.10975609756097561 2.4390243902439025E-2
Comparing non/minority counts by bins of salary
Minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 8 13 7 9 1 0 Non-minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 10 21 22 18 9 2
EthnicityJGClassAnalysis
Count of JGClass | Average of JGClass4 | StdDev of JGClass3 | Min of JGClass2 | Max of JGClass | ||||
6.6842105263 | 2.1573916077 | |||||||
6.5853658537 | 2.1485063403 | |||||||
6.6166666667 | 2.1427357575 | |||||||
-1.6688 | 2.0547 | |||||||
YearsWorkedSalaryAnalysis
YearsWorkedSalaryAnalysis!A1 | ||||||
Years worked and salary | ||||||
Average of Salary4 | ||||||
$ 48,476 | $ 9,496 | 0.29 | ||||
$ 54,300 | $ 10,909 | $ 76,500 | ||||
$ 58,150 | $ 11,703 | $ 77,500 | $ 43,200 | 7.36 | 0.73 | |
$ 64,800 | $ 14,319 | 8.82 | ||||
$ 69,500 | 9.00 | |||||
Average of Salary3 | ||||||
48475.5555555556 | 9496.0794674711 | |||||
54300 | 10908.8954527945 | |||||
58150 | 11702.6553763299 | |||||
64800 | 14319.1480193481 | |||||
AgeSalaryAnalysis
Age/Salary Analysis | |||||||
Age and Salary | Age and other variables | ||||||
StdDev of Salary3 | Max of Salary2 | ||||||
$ 39,792 | $ 4,773 | $ 48,100 | $ 33,300 | 3.0769230769 | 0.3846153846 | 4.3846153846 | |
$ 49,073 | $ 7,724 | $ 70,000 | 5.7307692308 | 0.5769230769 | 5.8461538462 | ||
$ 54,854 | $ 8,235 | 5.3076923077 | 6.6153846154 | ||||
$ 61,132 | $ 11,434 | $ 38,700 | 7.7647058824 | 0.6176470588 | 7.7058823529 | ||
$ 56,273 | $ 13,295 | 8.2666666667 | |||||
$ 59,220 | $ 15,388 | ||||||
AgeJobGradeClassAnalysis
Age and Job Grade Classification analysis | |||||
Average of JGClass5 | StdDev of JGClass4 | Max of JGClass3 | |||
1.2608503439 | |||||
1.7132964178 | |||||
1.6988684017 | |||||
2.0820941056 | |||||
2.2928460169 | |||||
2.19089023 | |||||
4.7142857143 | 1.45405836 | ||||
6.4090909091 | 1.702504063 | ||||
7.5777777778 | 2.1583757455 | ||||
7.6666666667 | |||||
DataCopy
47700 |
Cross-Class-Table
Cross Classification Table |
Summary Table
One-Way Summary Table |
Bar Chart
Bar Chart
Total Minority Not Minority 38 82 Ethnicity
Histogram
TableOfContents!A1
Freq. & % Distribution
Frequency Distribution for Salary | ||
midpts | Percentage | |
29999.9 | 0.0% | |
39999.9 | 35000 | 15.0% |
49999.9 | 2 | 8.3% |
59999.9 | 55000 | 24.2% |
69999.9 | 65000 | 22.5% |
79999.9 | 75000 | |
89999.9 | 85000 | 1.7% |
100.0% |
% Polygons 2 Groups
TableOfContents!A1
SideBySide Bar Chart
Side-By-Side Chart
Female Minority Not Minority 11 42 Male Minority Not Minority 27 40