Multiple Linear Regression is a statistical methodology within inferential statistics that models the relationship between one continuous dependent variable and multiple independent variables to generate predictions or assess influence. The theory posits that these relationships adhere to specific assumptions, including linearity, independence of errors, homoscedasticity, normal distribution of residuals, and an absence of multicollinearity among predictors; when violated, particularly by high correlations between independent variables which obscure individual variable effects on the coefficient estimates, model interpretability is compromised. Consequently, techniques such as dummy coding for nominal data or variance inflation factor (VIF) analysis are theoretically required to handle categorical inputs and ensure valid inference regarding parameter significance and overall explanatory power.
Multiple Linear Regression is a statistical methodology within inferential statistics that models the relationship between one continuous dependent variable and multiple independent variables to gene…