1. Statistical Models Used in the Forecasting of Automobile Sales Ben Nelson, Ling-Chih Chen, Hsiu-Jung Hu, Yuliy Nesterenko, Bin Shi, Kimberly Williams

2. Introduction Objective F Models Used F Variables Used F Multiple Regression F Time Series F Multiple Regression vs Time Series F Managerial Explanation F

3. The objective of this project was to select the most viable statistical model to forecast Auto-sales for the USA. Our exercise included model building, model validating and model selection. Objective

4. Models Used Multiple Regression Models F Time Series Decomposition F ARIMA Box Jenkins F

5. Multiple Regression Variables Dependent Variable (Y) F Independent Variables (X’s) F - Auto Sales - Number of unemployed persons (thousands) - Bank credit - Real personal income (billions of chanied) - Federal funds rate - Japan (yen per US) - Germany (Deutsche mark per US) - Manufacturing of autos & light trucks - Manufacturing of rubber & plastic products - Petroleum products consumption - Truck tonnage index

6. Multiple Regression Variables Independent Variables (X’s) -- continued F - Central & South America nuclear electric - Texas marketed production of natural gas (unit 10,000) - Imports on machinery (transportation equipment) - Exports on manufactured goods - S&P stock price index on transportation - Producer price index in finished consumer goods - Real wages & salaries in mining manufacturing - Expenditures on furniture & household equipment - S&P’s stock prices (500 common stocks) - Index of help wanted advertising - Passenger fares

7. Independent Variable Assessment Scatterplots: indicate a majority of the dependent variables have a linear relationship with the independent variable. F We transformed 2 variables: (Producer price index of finished consumer goods & S &P's stock prices of 500 common stocks ) by using their squared value.

8. Independent Variable Assessment Using the Pearson Correlation report for all variables, the variables with a coefficient of.75 or higher were picked. (There were 4 of the 21 variables which were >.75). F All Possible Regression Report F Model 1 : “ Auto-sales ” with variable 14: “ Exports of manufactured goods ” only. Model 2 : “ Auto-sales ” with variables 13 and 14: “ Exports of manufactured goods ” and “ Imports of machinery & transportation equity ”. Model 3 : “ Auto-sales ” with variables 14, 2, and 19: “ Exports of manufactured goods ”, “ Bank credit ” and “ Expenditures on furniture & household equity ”.

9. Multiple Regression Analysis Model Evaluation Section F Model 1Model 2Model 3 Probability0.000000 Adj R-Squared0.65170.65290.6479 Significance component variables 1 of 11 of 21 of 3 LinearityOK IndependenceOK NormalityRejected Equal varianceOK Model 1: Auto sales = 58,13.639 + 1.100639 (Exports of manufactured goods)

10. Time-Series Decomposition Time-Series Decomposition Analysis F

11. Time-Series Decomposition Time-Series Decomposition Analysis – cont. F

12. Time-Series Decomposition

13. Time-Series ARIMA

14. Regression vs. Time-Series Comparison of Forecast vs. Actual F

15. Managerial Summary ARIMA presents the best forecasting model F Jan. 1997....... 51,412.70 Feb. 1997....... 51,734.50 Mar. 1997....... 52,056.20 Apr. 1997....... 52,377.90