1) Are the variables cross section or time series data?;2) How do you imagine that the data were collected?;3) Is the sample size sufficient to yield a good estimate? Does it fulfill Doane's Rule or Evan's Rule?;4) State you hypothesis about the sign of the slope for each predictor variable.;5) Generate a correlation matrix for your predictors. Based on the matrix is collinearity a problem?;6) Run the regression in Megastat requesting VIF's. Do they suggest a problem?;7) Interpret the slope coefficient. Does the intercept have meaning given the range of the data?;8) Use Megastat to fit the regression model, including residuals and standardized residuals;9) Interpret the P-value for each slope coefficient;10) Interpret the R^2 value;11) Study the table of residuals. Identify outliers, and unusual observations (standardized obs. that exceed 3, and 2 std. deviations respectively).;12) Output a normal probability plot. Do you see evidence that your regression violates the assumption of normality?;13) Inspect the residual plot to check for heteroscedasticity. Report your conclusions.;14) Identify any observations with high leverage.
Paper#18147 | Written in 18-Jul-2015Price : $27