You work for an auto dealer and given a task to come up with a price prediction model for used Toyota Corolla. The data you have is based on the 1436 vehicles that the dealer sold, it has vehicle selling price, age (in months at the time of the transaction), and odometer reading (in kilometers), fuel type, horsepower, color (metallic or not), transmission type, engine displacement (in cc), number of doors, and vehicle weight. Using the attached data set do a regression analysis by following the steps below. Note: For every step, copy and paste the report or the few entries in the updated data frame here to this word document to show you completed the task. a. Introduce dummy variable(s) for appropriate attribute(s).
b. Split the data two: training data and validation data, where training data is 65% of the data set and use random state value 8.
c. Create a regression model without the intercept, run the OLS regression report and evaluate the results based on the training data
d. Introduce the intercept to the model, run the OLS regression report, and evaluate the results
e. Eliminate independent variables that do not increase the model's (in part d) explanatory power. Use p=0.05 as a threshold to eliminate independent variables
f. Given the final list of independent variables in part e, run the model on the validation data create OLS renart and internet the results