Both continuous and categorical variables can be used as independents.

It helps determine the individual and partial contribution of each independent variable in explaining the variance in dependent variable.

Interpretation of coefficients is similar to simple linear regression – they indicate the expected change in Y with each one unit increase in the corresponding X, keeping others constant.

Evaluation metrics like R-squared, F-statistic, p-values, etc. are used to assess overall model fit and significance.

Assumptions include linearity, no multicollinearity, homoscedasticity, independence of errors, normality of residuals.

Variable selection techniques help determine the optimal set of predictors.

So in summary, multiple regression extends linear modeling to multiple independent factors simultaneously.

Join the conversation

Good! Nice Class

Reply