Utilized SAS to find an optimal model to evaluate the variables effect on retail prices and predict retail price of a specific vehicle • Made comprehensive analysis of all the features such as Mileage, Car Model, etc. Resulting in an optimal regression model • Used the optimal model to make prediction, resulting in a good vehicle price forecast which similar to the actual prices.

1. CSC 423 Final Project GM Car Price Yawen Li ShuXie

2. Goals of Analysis 1.Find an optimal model to evaluate the variables affecting the retail prices. 2. Find out which variables are more important to the retail price of GM cars. 3. Use the optimal model to predict retail price of a specific vehicle.

3. Exploratory of the data GMCar Price Data Gplot of Price vs Mileage

4. Exploratory of the data GMCar Price Data Gplot of LogPricevs Mileage

5. Exploratory of the data GMCar Price Data Residual vs Predicted

6. Exploratory of the data GMCar Price Data Residual vs Predicted

7. Modeling Approach Transformation Interaction Collinearity Three Method Parameter significance Compare Models Residuals vs Predicted Residuals vsMileage Normal Probability Preprocessing Automatic Model Selection Residual Analysis

8. Modeling Approach Preprocessing Final Model Validation Prediction Automatic Model Selection Residual Analysis

9. Conclusion of Analysis Final Model:

10. Conclusion of Analysis Final Model: F value : 961.43 R2 : 0.9659 Adj R2 : 0.9649 Three outliers No Influential points The strongest predictors is Cylinder. Parameter Standard Standardized Variance Variable DF Estimate Error t Value Pr > |t| Estimate Inflation Cylinder_6 1 0.56472 0.01464 38.58 <.0001 0.670646.91884 Cylinder_8 1 1.09822 0.02312 47.51 <.0001 0.884287.93199

11. Thank you