Use the UsedCars2.xlsx dataset on Canvas to answer the following questions. This dataset represents characteristics of cars that are currently part of the inventory at a used car dealership. The variables included are car, year, age, price ($), mileage, power (hp), fuel (mph), region of origin (manufactured in USA or elsewhere), and single ownership (yes = owned by one owner, no = owned by more than one owner).
The dealership owner thinks that age, mileage, MPG and origin region may affect car price and would like to use these car characteristics to predict car price.
1. Write out the population model for the analysis described above, and estimate this population model using the sample data.
2. Interpret the estimated coefficient on age.
3. Suppose the dealership thinks the effect of car age on price depends on origin region. How would you include this in your model? Based on the new estimated model, does it appear the effect of age depends on origin region?
4. Based on your estimated regression equation, which variables should be included in the estimated model? Which should be excluded? (Hint: Think about which variables are statistically significant and which are not.)
5. Is the overall regression statistically significant?
6. Do you think this is a good model for predicting car price? Why or why not?
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