1. Chapter 12: Regression Diagnostics
E370, Spring 2016

2. Assumptions Data Data Accuracy Measurement accuracy Outliers
Observations that may dramatically affect the results of a regression
Model
Linear relationship
The dependent variable is related with the independent variables in a linear fashion.
Absence of Multicollinearity
Independent variables are not highly correlated with each other

3. Data Check
Check range, mean and other descriptive statistics for data accuracy
Outliers: Too far of the predicted value--- Standardized residuals’ absolute value higher than 3
Influential outliers: whose exclusion dramatically changed the coefficients.

4. Absence of Multicollinearity
Check pair-wise correlation coefficients
Correlation coefficients higher than 0.8 indicate multicollinearity

5. Assumptions Error Normality Normally distributed error term
Errors cancel on average
Error centered at 0
Homoscedasticity
Errors have a constant variance over the full range of the dependent variable.

6. Normally Distributed Error Centered around Zero
Histogram of residuals

7. Homoscedasticity
Scatter plot of residuals and predicted values of dependent variable