Part 1.
a. Run an ANOVA to compare the mean price of your three vehicle models. Insert a screenshot of the Printout.
b. Is there a significant difference between the means (just state yes or no)?
c. Run a Tukey post hoc test to rank the means of the prices of the models (even if there was not a significant difference). Insert a screenshot of your Printout. d. List your vehicle models (use vehicle names, not numbers!) in order from that with the highest mean price to that with the lowest mean price (indicate any “ties”).
PART 2.
a. Imagine that you have reason to believe that the majority of used vehicles for sale were newer cars (produced in the year 2016 or after). Test this assumption with a binomial test of proportions. Insert your Printout. (6 points)
b. Interpret the results of the above analysis. (3 points)
PART 3.
a. Run a simple linear regression of your student data set using the price of the vehicle as the dependent variable (y) and the mileage as the independent variable (x). Insert your Printout. (6 points)
b. Is this a useful model? Interpret the p-value based on a one-tailed test. (2 points)
c. What is the y-intercept of the model? (2 points)
d. Can the y-intercept be practically interpreted in this case (yes or no)? (2 points)
e. What is the slope of the model? (2 points)
f.What is the R² of the model? (2 points)
g. Interpret the R² of this model. (3 points)
Attachment below is the vehicle data from my previous project submission for this.
Model
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Price
Mileage Year Made
20000
50303
1
18000
70400
0
19500
60400
0
19900
50299
1
18000
51900
0
18000
61000
1
21000
45922
0
22100
37499
0
19600
78200
0
21800
35877
0
18500
56800
0
19600
45200
1
23000
23664
1
21500
43000
1
20000
56083
1
18000
54670
0
18700
68900
1
18500
65400
0
21000
45636
1
21600
54722
0
22000
45391
1
19800
45621
0
19900
45321
1
18800
63697
0
18000
64571
1
21100
64321
1
19200
45201
1
23000
45922
1
21500
37499
1
19600
78200
0
23100
35877
1
19900
56800
0
21000
45200
0
22300
23664
1
24500
45900
0
23700
56083
0
19900
54670
0
18400
68900
0
18700
45621
0
23900
45321
0
23000
63697
0
21700
64571
0
21500
64321
0
23000
45201
0
19000
45922
0
19700
37499
1
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
21700
22000
21300
23000
21500
20000
18000
18700
18500
21000
21600
22000
19800
19900
18800
18000
21100
19200
23000
21500
18600
23100
18900
21000
22300
24500
23700
19900
23000
78200
35877
56800
45200
23664
41000
45321
63697
64571
64321
45201
45922
37499
78200
35877
56800
45200
23664
45900
56083
54670
68900
45621
45321
63697
64571
64321
45201
33210
0
1
1
1
1
1
1
0
0
0
0
0
1
0
1
0
0
1
0
0
1
1
1
1
1
1
1
0
0
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