>Real Estate e
2 1 1 3
2 2 4
0 3 2 0 0 4 Fixed 820 0 0
4 3 0 1 21 2 Fixed 0 06 30
3 1 0 3 Fixed 7
0 6 10
6 0 1 4 Fixed 17 0 0
4 3 1 1 31 4 Fixed 19 0 0
4 2.5 0 1 4 10 813 0 0
0
5 0 1 26 2 Fixed 6 0 6120
5 3.5 0 1 26 4 Fixed 3 810 0 9
4
6 4 1 1 3 Fixed 6 0 Rose 00
80
6 4 0 1 4 Fixed 8 1 60
4420 4 3 0 1 50 2 Adjustable 9 1 10
3 2.5 0 1 3 Adjustable 9 1 Rose 6000
2 0 0 34 1 Fixed 20 8
0 Rose 421
2 0 4 3 1 1 17 3 Adjustable 10 0 3
0
6 4 1 1 12 3 Fixed 18 0 146
5 3.5 1 1 28 2 Adjustable 9 792 1 4430
00
5 3.5 1 1 36 3 Adjustable 10 8
0 269
3 2 1 1 38 3 Fixed 16 0 9
6
6 4.5 1 1 37 5 Fixed 2 0 2 1.5 1 0 20 4 Fixed 6 0 1950 2 1.5 1 0 52 1 Fixed 10 0 50
0
4 3 1 1 31 4 Fixed 8 0 160
3 2.5 0 1 40 1 Fixed 18 0 7
4 3 1 1 54 1 Fixed 20 6
0 5
5 3.5 1 1 26 5 Adjustable 9 0 20
5 3.5 0 1 23 4 Adjustable 9 1 4 3 0 1 31 3 Fixed 19 772 0 3 2 1 0 37 3 Fixed 5 0 2 1.5 1 1 31 5 Fixed 6 769 0 23
2 1.5 1 0 28 5 Adjustable 9 1 4 3 1 1 28 3 Fixed 8 1 4 3 1 1 22 4 Fixed 2 759 1 2 1.5 1 0 22 2 Fixed 17 758 0 3 2 1 1 23 3 Fixed 5 1 4 3 0 1 25 4 Fixed 12 0 4 3 0 1 37 2 Fixed 18 0 3 4 3 1 1 15 4 Fixed 10 0 44
4 3 1 1 19 3 Fixed 15 749 0 3 2 0 0 31 2 Fixed 13 0 4 3 0 1 19 3 Fixed 5 0 92
4 3 1 1 27 1 Adjustable 9 1 732
5 3.5 1 1 29 5 Fixed 4 740 0 6 4 1 1 19 5 Adjustable 10 0 00
3360 3 2 0 0 32 3 Fixed 6 0 3 2 1 0 28 1 Fixed 10 737 0 5
0
3 2.5 1 0 30 4 Fixed 8 0 3 2 0 1 23 4 Fixed 6 736 0 4 3 0 1 27 5 Fixed 13 0 0
6 4 1 1 35 5 Adjustable 10 0 8 1 1 27 4 Fixed 6 0 2 1.5 1 0 39 2 Fixed 15 0 4 3 1 1 30 4 Fixed 17 0 3 2 0 1 26 3 Fixed 10 726 0 2 1.5 1 0 14 3 Fixed 18 0 2 1.5 1 1 27 2 Fixed 15 0 6 4 0 1 49 1 Fixed 5 0 5 3.5 1 1 29 5 Fixed 8 710 0 3290 5 3.5 1 1 24 2 Fixed 14 0 4 3 0 1 18 5 Fixed 11 0 4 2.5 1 1 27 4 Fixed 10 0 2 1.5 1 0 18 3 Fixed 10 0 2 1.5 1 1 30 4 Adjustable 2 675 0 2520 3 2.5 0 0 2 4 Adjustable 5 1 Marty 4 3 1 1 22 4 Adjustable 2 0 3 2 0 0 30 1 Adjustable 1 673 0 4
2 1.5 1 1 28 1 Adjustable 6 0 2090 3 2 0 0 30 2 Adjustable 8 669 1 2300 3 2.5 1 1 50 2 Adjustable 4 667 0 60
5 3.5 0 1 42 4 Adjustable 3 665 0 6 4 1 1 21 3 Adjustable 8 1 4370 4 2.5 0 1 24 1 Adjustable 2 0 3160 5 3.5 1 1 22 5 Adjustable 3 0 7 5 1 1 40 3 Adjustable 7 1 3 2 0 0 14 4 Adjustable 7 1 2 1.5 1 1 25 3 Adjustable 5 1 4 3 1 1 32 2 Adjustable 2 0 2 1.5 1 0 21 2 Adjustable 3 0 00
1690 2 1.5 0 0 20 1 Adjustable 7 639 1 2 1.5 1 1 31 4 Adjustable 6 1 4660 4 3 1 1 34 3 Adjustable 7 630 1 3 2.5 1 1 48 5 Adjustable 3 0 60
3840 6 4.5 0 1 32 2 Adjustable 5 626 1 3180 3 2 1 1 40 1 Adjustable 6 1 8 5.5 1 1 30 4 Adjustable 1 0 3 2 1 0 40 2 Adjustable 8 1 2 1.5 1 1 36 4 Adjustable 3 618 1 6 4 0 1 23 1 Adjustable 7 0 3 2.5 0 1 23 1 Adjustable 6 614 1 42
6160 6 4 1 1 24 3 Adjustable 7 0 4 2.5 0 1 38 3 Adjustable 3 609 1 2 1.5 1 0 39 5 Adjustable 1 609 0 7 5 1 1 53 4 Adjustable 3 605 1 2 1.5 1 0 58 4 Adjustable 1 0 3 2 1 1 27 2 Adjustable 6 1 3 2 0 1 35 2 Adjustable 8 599 1 6 4 1 1 50 4 Adjustable 8 1 3 2.5 0 0 28 1 Adjustable 6 1 6 4 1 1 12 4 Adjustable 2 595 0 3060 3 2 1 1 27 3 Adjustable 3 0 Peterson 2 1.5 0 0 37 3 Adjustable 6 591 1 Isaacs 2 1.5 1 0 11 5 Adjustable 8 591 1 Marty 2 1.5 1 1 30 2 Adjustable 7 1 Peterson 4 3 1 1 27 5 Adjustable 3 584 1 2 1.5 0 1 34 5 Adjustable 8 583 1 FICO Residual Plot FICO Residuals Type code Residual Plot Normal Probability Plot 5 Carter 6 4.5 0 1 13 4 Fixed 17 0 6 4 1 1 32 3 Fixed 6 808 0 4 3 1 1 17 3 Adjustable 10 0 4920 6 4.5 1 1 37 5 Fixed 2 0 3180 4 3 1 1 54 1 Fixed 20 0 1920 2 1.5 1 1 31 5 Fixed 6 769 0 3360 3 2 0 0 32 3 Fixed 6 737 0 6 4 1 1 35 5 Adjustable 10 731 0 5760 5 3.5 0 1 42 4 Adjustable 3 665 0 6160 6 4 1 1 24 3 Adjustable 7 613 0 Name: _________________________________________
Data Review Project
150 points Please be sure to do each task listed to receive full credit. All information must be word processed and placed name is one the upper left-hand corner of the first page of your assignment. North Valley Real Estate Project 1. Look over the following variables in the report on homes sold in the area last year: selling price, a. Which of the variables are qualitative and which are quantitative? 2. For the variable price, organize the selling prices into a frequency distribution using five class-intervals. class? homes sold for last year? What is the lowest price that the top ten percent of the homes sold? g. Construct a box plot for sales price. a. What is the mode of each of these variables axis. 5. Sort the data into a table that shows the number of homes that have a garage attached versus those a. The home has a garage attached. 1 6. Create a probability distribution, the mean, and the standard deviation for the following variables: 7. The mean days on the market is 30 with a standard deviation of 10 days. Create a frequency a. What do you observe in the frequency distribution? Compare this to the actual results. than the mean number of days? Compare this to the actual number of homes. Is the normal 8. Assume the 105 homes is a population. A sample is taken using the following records: 5, 10, 15, 20, a. Calculate the sample mean and sample standard deviation for the sales price. i. Mean selling price 9. A new agent was assigned the 20 homes to sell. The agent reviewed his sample and felt it was a. Does the agent feel his sample is more or less than the firm’s average price? 10. Conduct a two-sample hypothesis test for the following scenarios at a 0.05 significance level: homes without? garage and homes without an attached garage? on the mortgage? a. At the 0.02 significance level, is there a difference in the variability of the selling prices of the b. At the 0.10 significance level, is there a difference in the mean selling price of the homes among c. At the 0.10 significance level, is there a difference in the mean selling price of the homes among 2
12. For the following scenarios determine the regression equation, then use the information
a. Let the selling price be the dependent variable and size of the home the independent variable. b. Let days-on-the-market be the dependent variable and price be the independent variable. 13. Use selling price of the home as the dependent variable and determine the regression equation using a. Which independent variables have a significant relationship with the dependent variable? R-square results. met? 3
2
1
7
Record #
Agent
Price
Size
Bedrooms
Baths
Pool (yes is 1)
Garage (Yes is 1)
Days
Township
Mortgage type
Years
FICO
Default (Yes is 1)
1
Marty
2
0
6
4
24
1
8
20
1.
5
3
Fixed
82
2
Rose
34
61
50
30
10
36
9
3
Carter
37
23
60
3
21
18
8
19
4
Peterson
31
22
33
2.5
26
17
81
5 Carter
49
100
45
4.5
13
8
16
6 Peterson
29
40
86
3
44
813
7 Carter
2
28
810
2
63
39
Adjustable
8
Isaacs
38
4
42
4
47
3.5
8
12
9 Peterson
41
4040
10 Isaacs
48
74
43
80
32
808
11
44
88
52
35
806
12 Peterson
38
89
805
13 Marty
33
56
29
70
25
801
14
27
2300
1.5
79
15
3
46
97
7
95
16 Isaacs
4
53
91
3
66
7
92
17 Carter
3
76
32
90
18 Peterson
69
59
78
19 Rose
2
51
2050
786
20 Rose
54
75
4920
7
85
21 Marty
214910
1950
7
84
22 Rose
18
87
99
782
23 Carter
4
599
4
68
781
24 Isaacs
2
64
2540
780
25 Carter
3
93
55
3180
77
26 Isaacs
478
67
4660
7
73
27 Carter
3840
4220
7
72
28 Marty
313200
3600
29 Isaacs
274482
2990
769
30 Marty
1679
62
1920
31 Isaacs
1
7
58
1970
766
32 Isaacs
2264
98
2520
763
33 Carter
316827
3150
34 Carter
189984
1550
35 Marty
366350
3090
754
36 Isaacs
41
6160
4080
753
37 Isaacs
308000
3500
752
38 Rose
2
94
57
2620
751
39 Carter
33
71
2790
40 Peterson
299730
2910
748
41 Rose
445
740
4370
746
42 Rose
4
105
4200
741
43 Peterson
667
5570
44 Rose
523
584
5050
739
45 Marty
3360
737
46 Marty
202598
2270
47 Marty
32
669
2
83
736
48 Rose
321320
2770
49 Isaacs
246820
2870
735
50 Isaacs
546084
591
731
51 Isaacs
793084
6800
5.5
729
52 Isaacs
174528
1600
728
53 Peterson
392554
3970
726
54 Peterson
26
3160
3060
55 Rose
237120
1900
723
56 Carter
225750
2150
715
57 Isaacs
848420
7190
710
58 Carter
371956
3110
59 Carter
404538
707
60 Rose
250090
2810
704
61 Peterson
369978
3830
703
62 Peterson
209292
1
630
701
63 Isaacs
190032
1850
64 Isaacs
216720
674
65
323417
3220
673
66 Isaacs
316210
3070
67 Peterson
22
605
2090
670
68 Marty
183920
69 Rose
248400
70 Isaacs
4
665
5760
71 Rose
667212
6110
662
72 Peterson
362710
656
73 Rose
265440
653
74 Rose
7065
96
6600
652
75 Marty
293700
3300
647
76 Marty
199448
2330
644
77 Carter
369533
4230
642
78 Marty
230121
2030
639
79 Marty
1690
80 Peterson
190291
2040
631
81 Rose
393584
82 Marty
363792
2860
626
83 Carter
3
609
84 Carter
310877
624
85 Peterson
919480
7670
623
86 Carter
392904
3400
618
87 Carter
200928
1840
88 Carter
537900
4890
614
89 Rose
258120
2390
90 Carter
5
583
613
91 Marty
302720
3440
92 Isaacs
240115
2220
93 Carter
793656
6530
94 Peterson
218862
1930
604
95 Peterson
383081
3510
601
96 Marty
351520
3380
97 Peterson
841491
7030
596
98 Marty
336300
2850
595
99 Isaacs
312863
3750
100 Carter
275033
593
101
229990
2110
102
195257
2130
103
194238
1650
590
104
348528
2740
105 Peterson
241920
2240
Sample
Record # Agent Price Size Bedrooms Baths Pool (yes is 1) Garage (Yes is 1) Days Township Mortgage type Years FICO Default (Yes is 1)
496100
4510
816
10 Isaacs 487494
4380
15 Rose 346421
2970
795
20 Rose 547596
785
25 Carter 393557
776
30 Marty 167962
35 Marty 366350 3090 3 2 1 1 23 3 Fixed 5 754 1
40 Peterson 299730 2910 3 2 0 0 31 2 Fixed 13 748 0
45 Marty 336000
50 Isaacs 546084 5910
55 Rose 237120 1900 2 1.5 1 0 14 3 Fixed 18 723 0
60 Rose 250090 2810 4 3 0 1 18 5 Fixed 11 704 0
65 Marty 323417 3220 4 3 1 1 22 4 Adjustable 2 673 0
70 Isaacs 466560
75 Marty 293700 3300 3 2 0 0 14 4 Adjustable 7 647 1
80 Peterson 190291 2040 2 1.5 1 1 31 4 Adjustable 6 631 1
85 Peterson 919480 7670 8 5.5 1 1 30 4 Adjustable 1 623 0
90 Carter 558342
95 Peterson 383081 3510 3 2 1 1 27 2 Adjustable 6 601 1
100 Carter 275033 3060 3 2 1 1 27 3 Adjustable 3 593 0
105 Peterson 241920 2240 2 1.5 0 1 34 5 Adjustable 8 583 1
in bullet points unless otherwise specified. Please make sure that your name, the name of the class, and my
(Appendix A; page 746)
number of bedrooms, township, and mortgage type.
b. How is each variable measured? Determine the level of measurement for each of the variables.
a. What values of the price tend to cluster?
b. What is the typical selling price in the first class? What is the typical selling price in the last
c. What is the mean sales price? What is the standard deviation?
d. What is the price range? What is the median sales price?
e. About 95% of the sales prices are between what two values?
f. Create a cumulative relative frequency distribution. What price (or less) does fifty percent of the
What percent of the homes sold for less than $300,000?
3. Construct a bar chart for the following variables: Bedrooms, bathrooms, and township.
4. Develop a scatter diagram with price on the vertical axis and the size of the home on the horizontal
a. Is the relationship between the two variables indirect or direct?
b. Develop the same scatter diagram but include only homes without a pool.
c. Develop the same scatter diagram but include only homes with a pool.
d. Compare the relationships with and without a pool.
that don’t in each of the five townships. If a home is selected at random, compute the following
probabilities:
b. The home does not have a garage attached, given that it is in Township 5.
c. The home has a garage attached and is in Township 3.
d. The home does not have a garage attached or is in Township 2.
Derived from assignments from Lind, Statistical Techniques in Business and Economics, 17e
a. The number of bedrooms
b. The number of bathrooms
distribution of days on the market.
b. Use the normal distribution to estimate the number of homes on the market more than 24 days.
c. If days on the market is normally distributed, how many homes should be on the market more
distribution a good approximation of the actual results?
25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100.
b. Using your answers from #2 part c, compare this to the sample mean and standard deviation.
c. Determine the likelihood of a sample mean price this high or higher.
d. Based on this sample, develop a 95% confidence interval for the following variables
ii. Mean days on the market
iii. Proportion of homes with a pool
significantly different from that of the firm’s average selling price.
b. What would be the null and the alternative hypothesis statements?
c. Conduct a hypothesis test to see if the agent is correct with 0.05 significance level.
a. Can we conclude that there is a difference in the mean selling price of homes with a pool and
b. Can we conclude that there is a difference in the mean selling price of homes with an attached
c. Can we conclude that there is a difference in the mean selling price of homes that are in default
11. Test the following scenarios using ANOVA. Note the significance level changes based on the scenario.
homes that have a pool versus those that do not have a pool?
the five townships?
the selling agents?
Derived from assignments from Lind, Statistical Techniques in Business and Economics, 17e
Then, estimate the selling price for a home with an area of 2,200 square feet and provide a 95%
prediction interval for the selling price of a home of this size.
Then, estimate the days-on-the-market of a home that is priced at $300,000 and provide a 95%
prediction interval for days-on-the-market for that price.
the size of the house, number of bedrooms, days on the market, and number of bathrooms as
dependent variables.
b. Do you see any multicollinearity problems?
c. Determine a multiple regression equation based on your analysis in part a and b and your
d. Evaluate the addition of the variables: pool or garage. Report any changes in your results.
e. Develop a histogram of the residuals from your final regression. What normality assumption
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Derived from assignments from Lind, Statistical Techniques in Business and Economics, 17e