QMB 3200 FALL 2022 Project Submission 2Complete your work in this document in black type. Leave the existing document in the blue
type it is currently in and do not edit the document except to add your responses below each
prompt. Submit to Canvas as a single .doc or .docx file; files submitted in inaccessible formats
will not be graded. Check to make sure you submit the correct file; if you need to re-submit
after the due date there will be a penalty of 25%. The assignment is due by Sunday, October
23 at 11:59pm; late work will be penalized 10% per day or portion of a day that it is late. You
will use your student data set (cars) to run all analyses.
Part 1.
a. Run an ANOVA to compare the mean price of your three vehicle models. Insert a
screenshot of the Printout. (6 points)
b. Is there a significant difference between the means (just state yes or no)? (2 points)
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. (6 points)
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”). (3 points)
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)
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