Here are important assumptions that one has to check when performing a linear
- Assumption 1: Homoscedasticity of residuals or equal variance (with Breusch-Pagan test for example)
- Assumption 2: Normalcy of residuals (with Shapiro-Wilk test for example)
- Assumption 3: No autocorrelation of residuals (for time series with Durbin Watson test for example)
Here is my question: When performing a multiple linear regression (with more than ond independent variable), should one do each of the above-mentioned test on the entire model or should one run each of the above-mentioned test for each individual independent variable with the dependent variable?