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Develop Multiple Regression Model to Predict Index Fund Performance

1. Choose your favorite Index fund (e.g., DJIA, S&P 500, Wilshire 5000) and get at least 25 years of historical data of index performance.

2. Choose at least three market indicators (e.g., unemployment, GDP, 10-Yr. Treas. Rate, CPI, Initial Jobless Claims, inflation of US dollar) and get at least 25 years of historical data.

3. Create a multiple regression model that predicts the value of the index fund based on the three market indicators that you have chosen.

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4. Perform a residual analysis to determine the validity of the regression model

5. Determine the usefulness of each of the independent variables using by performing a hypothesis test on each of the slopes for the independent variables.

6. Use a Durbin Watson statistic to determine if autocorrelation is present in your data.

7. Check for interaction between your variables.

8. Predict the average index value based on a 95% confidence level for three different combinations of values of your market indicators.

Report should include the following:

1. 10 page limit, double spaced

2. Sections on Introduction, Methods, Results and Conclusions

3. Appropriate figures and tables to describe your data, analysis and results

4. List all assumptions made in your analysis

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