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What is regression diagnostics?

Regression diagnostics is a process used to assess the validity and accuracy of a regression model. Here are some key aspects of regression diagnostics:

1. Residual Analysis

Residuals: Residuals are the differences between the observed values and the predicted values of the model. Analyzing residuals helps identify patterns or systematic errors in the model.

2. Scatterplots

Scatterplots: Graphical representations, such as scatterplots of residuals against independent variables, can reveal outliers or non-linear relationships.

3. Normal Distribution of Residuals

Normal Distribution: Residuals should be normally distributed. Deviations from normal distribution may indicate issues in the model.

4. Homoscedasticity

Homoscedasticity: The variance of residuals should be constant. Changes in variance may suggest that the model is not equally suitable for all observations.

5. Multicollinearity

Multicollinearity: Check for high correlations between independent variables, as this can affect the stability of the model.

6. Influential Points

Influential Points: Identify observations that have a significant impact on the model's parameters. Outliers can strongly influence the results.

Regression diagnostics are crucial to ensure that a regression model is appropriate and reliable. It aids in identifying issues and optimizing model accuracy.