Also, using normalized data allows you to interpret the model weights more easily (larger magnitudes mean more effect on the predicted y value). Linear regression with interactions is most often used ...

The simulated model included one main effect and two 2-way interactions. The variable selection procedures included automatic selection (stepwise, forward and backward), common 2-step selection ...

The standard linear regression model does not apply when the effect of one explanatory variable on the dependent ... is a function of the other variable. This is called an interaction between the ...

Control strategies for models with interaction and quadratic effects ... Using predictions and marginal effects to compare groups in regression models for binary outcomes. Sociological Methods & ...

Logistic regression is the appropriate tool for such an ... To specify a forward selection model with main effects and their interactions, follow these steps: Figure 11.16 displays the "Testing Global ...

Multicollinearity exists when there is a correlation between multiple independent variables in a multiple regression ... model or finding some way to consolidate them to capture their joint effect ...