A Strategy for Developing Multiple Linear Regression (MLR) Models
4 min readSep 5, 2021
1. Hypothesize the model and state the LINE assumptions:
· If you have a few predictors, start with the first-order (main effect) linear model; otherwise use the best subsets and stepwise regression methods to select a few alternative models.
· LINE assumptions are:
1. L: Model is linear in terms of the parameters;
2. I: Errors are distributed independently;
3. N: Errors are distributed normally;
4. E: Errors have equal variance.
2. Fit the model to data; that is, obtain the model parameter estimates using the LSE method.
3. Check the validity of LINE assumptions by performing residual analysis:
· Obtain standardized residuals.
· Check for normality assumption by using:
1. Normal probability plot (NPP) of standardized residuals
2. Histogram…