The objective set forth in a recent staff meeting at D. L. Green and Associates is to develop a regression model for predicting company stock price. The data file Fast 100, contains data on several potential independent variables. What variables appear to have the most promise as predictors of stock price?
Model specification
- Identify the dependent variable and the independent variables to be included in the model
- Explain the expected sign (+, -, ?) for each of the independent variables
- Provide a correlation matrix with all independent variables and indicate extent to which multicollinearity may be a problem and what you might do to alleviate it.
Model Estimation
- Report the coefficients and absolute value of t-statistics for the coefficients of each independent variable
Model Evaluation
- Test for the statistical significance of each independent variable and when significant, the level of significance.
- Interpret the coefficient for each independent variable, remembering that an insignificant coefficient is interpreted as zero or having no effect on the dependent variable. Does the coefficient make sense?
- Evaluate the performance of the total model and the statistical significance of the model's (all variables together) explanatory power.
- Highlight key conclusions of the regression.