Model Diagnostics

Regression analysis diagnostics involve checking the assumptions made for the analysis. Four primary assumptions of regression analysis are listed below.

  1. Relationship between the dependent and independent variables is approximately linear

  2. Data is free from unusual observations, including, outlier, leverage, and influential points

  3. Errors (residuals) are normally distributed with zero mean and constant (homogeneous) variance, and uncorrelated, and

  4. Free from multicollinearity

Violations of these assumptions can be checked by both (1) visually looking at the data, and (2) performing statistical tests. The initial step is the visual look at the data using the scatter plots provided when using MS Excel. Any software can be used for the scatter plots. However, the author finds MS Excel is the most convenient and useful for a quick look at the data. The final step is to perform statistical tests to confirm the suspected violations of the regression analysis assumptions.