Your team is establishing a used car dealership and would like to develop a pricing model to help set sticker prices.
You have been given a set of car sales data for a large set of cars of the same make and model you intend to sell.
An investor group would like to invest $5 million, but they want to see that you have a solid tool to help you price cars appropriately.
Review the car sales data on the "Cars Lab Data" tab. Identify which variables are most likely to give you the best model. Apply your general knowledge about car prices.
You have decided to exclude Trim and Model. Generate a correlation matrix for the remaining variables. Comment on the values and what concerns if any you might have with the variables. NOTE: Make should be set-up as an indicator variable.
Develop two simple regression models for Price. For one model use Mileage as the independent variable. For the 2nd model use Make as the independent variable. Define the model equation for each regression. Comment on the results of the two models and how they predict price.
Run a multiple regression model for price with all variables EXCEPT Trim and Model. Comment on the results and the validity of the model parameters as predictors.
Starting with your model from Q4, follow a step-wise regression process to eliminate independent variables that fail one of the regression assumptions or statistical tests (i.e., p-values). Write your final model equation. Comment on the final regression results.
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