Usability Journal
For the Usability Journal assignment, you are to write three (3) journal entries.
- Each entry shall be approximately 300-400 words each.
- Each entry shall focus on an object that contains a bad usability design.
- Explain why the object being described does not maintain good usability practices.
- The deliverable shall include citable examples of best practices from academic literature explaining why the product/object does not follow best practices.
Example:
- A remote control.
What about the remote control makes the device both a good and bad example for the usability perspective?
Each deliverable will contain:
Three entries with 300-400 words for EACH entry
APA citation with examples from scholars explaining best practices that should be followed
An example image of the bad design practice followed
An example of good potential practices to followplease find the attachment for Problem solving questions.
2
Running Head: INTERACTIVE DESIGN
Interactive Design
·
Application Problem #
1
INSTRUCTIONS:
After reiewing the weekly readings and powerpoints, please complete the following:
1. Describe one specific example in which a regression/Pearson Product moment correlation could be used to help make an informed decision. Please write in complete sentences. (10 points)
2.
Make a table that contains data that would be used to perform an analysis. Use your answer from #1 as the basis for your table. YOU DO NOT HAVE TO COLLECT DATA FROM AN OUTSIDE SOURCE. Make up the data that would make sense in the situation that you described in #1. (1
5
points)
3
. Run the analysis in JASP, Excel, or another program and record/upload your results. (10 points)
4
. Explain what decision you would make based on the results of the analysis that you performed. (15 points)
Example:
1. A coach wanted to know if the number of hours of practice per week shooting free throws improved the players skill in shooting free throws over the
8
week basketball season.
2.
|
Week |
Hours of practice |
Number of free throws made (out of 100 shots) |
|||||
| 1 |
1 7 |
||||||
| 5 |
5 6 |
||||||
| 3 |
2 9 |
||||||
| 4 |
11 |
84 |
|||||
|
59 |
|||||||
| 6 |
38 |
||||||
| 7 | 8 |
64 |
|||||
|
27 |
3. The value of R is 0.9606. The P-Value is .000148. The result is significant at p < .05.
4. As a coach, I see that the data shows a strong correlation between the number of hours of practice and the number of free throws made. Therefore, I would encourage my players to practice free throws as much as possible. The data shows a strong positive correlation.
·
Application Problem #2
INSTRUCTIONS: Read the paragraaph below, then answer the questions following the paragraph.
Janice is a production manager for a company that designs and produces hydraulic valves that are used in aircraft systems. The company is concerned that the number of valves not meeting the strict measurement parameters has incereased over the past several months. She implements a quality control program that includes random inspections. Employees are notified that these inspections will help the company to reduce expenses from poor product quality. Janice runs the program for 9 months and performs a different number of inspection each month. The table below shows the number of inspections and the number of faulty hydraulic valves produced each month.
|
Inspections |
Number of faulty valves |
| 9 | |
Run the analysis and answer the following questions:
1. Does the number of inspections result in fewer faulty valves? (Run the analysis and report the results)(25 Points)
2. What decision should Janice make regarding the quality control inspections? (25 points)
>VANN EX.
verage 8
8
1
15 75 78 23 91 18 78 15 75 Statistics
s
19 16 78 239.5373175119 17 18 Standard Error 63881174
-30.8766710289 -17.3361052058 0.0000000001 0.4436233635 0.6048007399 1 4 Standard Error 3 ANOVA df SS MS F Significance F Regression 1 4.5 27 Residual 1 0.1666666667 Total 2 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% -23.7494156765 13.4160823431 0.1210377183 -2.1679653624 5.1679653624 Observation 0.1666666667 0.1666666667 Number correct on 6 qs. Quiz Residual Plot 4 5 6 0.16666666666666696 -0.33333333333333304 0.16666666666666785 Number correct on 6 qs. Quiz Test A 1 10 7 8 3 3 df SS MS F Significance F 52.0713450292 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% -3.7488773068 3.8541404647 0.0044653283 0.4729938944 1.7480587372 RESIDUAL OUTPUT Residuals Test B Line Fit Plot Test A 7 8 6 9 4 6 5 3 2 10 9 9 8 6 5 5 3 1 Predicted Test A 7 8 6 9 4 6 5 3 2 7.8263157894736857 8.9368421052631604 6.7157894736842119 10.047368421052635 4.4947368421052634 6.7157894736842119 5.6052631578947372 3.3842105263157896 2.2736842105263153 Test B >Problem 5
6 40 4 76 Statistics
3601193
] = .17, p > .05).
5878 for the regression/Pearson is N-2.
s
294
16 Training Profit Observations 16 16 4
1.1338051904 14 67721
0 15 40 df 15 Standard Error t Stat 0984
001
1.4607204684 7.1481100001 0.5330957594 -0.0681239475 0.0368465629 Practice Score Mean Variance Observations 10 10 Pearson Correlation 0.9274655051 Hypothesized Mean Difference 0 63.9983043764 P(T<=t) two-tail
t Critical two-tail -16.4906502417 -5.0737416623 0.0001108777 0.131224418 0.2597242954 Turnitin Report Formatting Title Page Citation Outline
2
ACT Scores
Test A
2
1
8
Is there a relationship between ACT scores and test averages in _________class?
18
7
2
3
9
17
7
6
26
93
27
97
22
8
4
1
5
75
19
80
18
79
20
86
13
74
24
90
16
21
89
18
82
20
85
VANN EX. ANS
ACT Scores
Test Average
21 88
23 91
17 76
26 93
27 97 22 84
SUMMARY OUTPUT
19 80
Regression
18 79
Multiple R
0.9577121814
20 86
R Square
0.9172126225
13 74
Adjusted R Square
0.9123427767
24 90
Standard Error
1.1277405111
15 75
Observation
23 91
ANOVA
21 89
df
SS
MS
F
Significance F
18 82 Regression 1
239.5373175119
188.3453136024
0.0000000001
20 85
Residual
21.620577225
1.2717986603
Total
261.1578947368
Coefficients
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-24.
10
3.2089453612
-7.5122463625
0.0000008503
-30.8766710289
-17.3361052058
Test Average
0.5242120517
0.0381970297
13.7238957152
0.4436233635
0.6048007399
(r [17] = .95, p < .01). There is a relationship between ACT scores and the test score.
Prac
Hours studying
Number correct on 6 qs. Quiz
1 4
2 5
4 6
Prac ANS
Hours studying Number correct on 6 qs. Quiz SUMMARY OUTPUT
2 5
Regression Statistics
4 6 Multiple R
0.9819805061
R Square
0.9642857143
Adjusted R Square
0.9285714286
Answer:
0.4082482905
Observations
(r [1] = .98, p > .05
There is no relationship between
studying and number correct.
4.5
0.1210377183
This sample size was very small
0.1666666667
You want at least 30 in each
4.6666666667
group you examine to rule
out random chance.
Intercept
-5.1666666667
1.4624940646
-3.5327778702
0.1756097164
-23.7494156765
13.4160823431
Number correct on 6 qs. Quiz
1.5
0.2886751346
5.1961524227
-2.1679653624
5.1679653624
RESIDUAL OUTPUT
Predicted Hours studying
Residuals
1
0.8333333333
2
2.3333333333
-0.3333333333
3
3.8333333333
ResidualsPPT EX
Subject
Test B
1 10 7
2 9 8
3 9 6
4 8 9
5 6 4
6 5 6
7 5 5
8 3 3
9 1 2
PPT ANS
Subject Test A Test B SUMMARY OUTPUT
2 9 8 Regression Statistics
r [7] = .84, p < .01)
3 9 6 Multiple R
0.84137898
There was a correlation between
4 8 9 R Square
0.707918588
test A and test B.
5 6 4 Adjusted R Square
0.666192672
6 5 6 Standard Error
1.7519055147
7 5 5 Observations 9
9 1 2 ANOVA
Regression 1
52.0713450292
16.9659208449
0.0044653283
Residual 7
21.4842105263
3.0691729323
Total 8
73.5555555556
Intercept
0.0526315789
1.6076587573
0.032738029
0.9747974224
-3.7488773068
3.8541404647
Test B
1.1105263158
0.2696125699
4.1189708478
0.4729938944
1.7480587372
Observation
Predicted Test A
1
7.8263157895
2.1736842105
2
8.9368421053
0.0631578947
3
6.7157894737
2.2842105263
4
10.0473684211
-2.0473684211
5
4.4947368421
1.5052631579
6 6.7157894737
-1.7157894737
7
5.6052631579
-0.6052631579
8
3.3842105263
-0.3842105263
9
2.2736842105
-1.2736842105
Test A
2
1
Training
Profit
Is there a relationship between hours of training and amount of profit per employee?
2
7
5
3
6
4
76
5
54
1
75
2
4
9
3
3
0
4 49
5 30
6
40
1
50
2
20
3 40
4
60
5
70
Problem 1 Answer
Training Profit Is there a relationship between hours of training and amount of profit per employee?
2 75
3
65
SUMMARY OUTPUT
5 54
Regression
There is no relationship between hours of training and amount of profit per employee
1 75
Multiple R
0.
16
8
(r [
14
2 49
R Square
0.02834512
98
Notice that running the regression and looking at the multiple r, or looking at the Pearson on the t-test give you the same correlation.
3 30
Adjusted R Square
-0.04
10
95
Remember, the
df
t-Test: Paired Two Sample for
Mean
4 49
Standard Error
1.6661
80
5 30
Observations
6 40 Mean
3.5
51.4375
1 50
ANOVA
Variance
2.6666666667
309.0625
2 20 df
SS
MS
F
Significance F
3 40 Regression 1
1.1338051
90
0.4084082001
0.5330957594
Pearson Correlation
-0.1683601193
4 60
Residual
38.8661948096
2.776
15
Hypothesized Mean Difference
5 70
Total
6 40
t Stat
-10.6958487157
Coefficients
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
P(T<=t) one-tail
0.0000000102
Intercept
4.3044152342
1.3258636777
3.2464
99
0.005853328
1.4607204684
7.1481
100
t Critical one-tail
1.7530503557
Profit
-0.0156386923
0.024471084
-0.639068228
-0.0681239475
0.0368465629
P(T<=t) two-tail
0.0000000205
t Critical two-tail
2.1314495456
Problem 2
Practice
Score
Is there a relationship between the amount of time that students practice statistics and their exam score?
2 60
3 70
4 80
5 90
6 90
7 95
8
97
9 98
10 99
10 100
Problem 2 Answer
Practice Score Is there a relationship between the amount of time that students practice statistics and their exam score?
2 60
t-Test: Paired Two Sample for Means
3 70 SUMMARY OUTPUT
There is a relationship between amount of time practicing statistics and
4 80
student performance, (r [8] = .93, p < .01).
5 90
Regression Statistics
6.4
87.9
6 90 Multiple R
0.9274655051
8.2666666667
186.1
7 95 R Square
0.8601922631
8 97 Adjusted R Square
0.842716296
9 98 Standard Error
1.1402683688
10 99 Observations 10 df 9
10 100 t Stat
-23.370660174
ANOVA P(T<=t) one-tail
0.0000000011
df SS MS F Significance F t Critical one-tail
1.8331129327
Regression 1
63.9983043764
49.2214398053
0.0001108777
0.0000000023
Residual 8
10.4016956236
1.300211953
2.2621571628
Total 9
74.4
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept
-10.782195952
2.4754744376
-4.3556078739
0.0024269198
-16.4906502417
-5.0737416623
Score
0.1954743567
0.0278620223
7.0157992991
0.131224418
0.2597242954
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