Multivariate regression plot accompanied by the R-value in R

Multivariate regression (often called multiple regression) is a statistical method used to examine the relationship between one dependent variable and two or more independent variables. It extends simple linear regression, which only considers one predictor, allowing researchers or analysts to account for the influence of multiple factors simultaneously. Purpose To understand how multiple independent variables (predictors) collectively affect a dependent variable (outcome). To quantify the strength and direction of each predictor’s effect while controlling for others. An 𝑅 square close to 1 indicates a strong fit; close to 0 indicates weak explanatory power. Applications Economics: Predicting income based on education, experience, and age. Medicine: Assessing how lifestyle, genetics, and treatment influence health outcomes. Marketing: Estimating sales based on price, advertising, and seasonality. Assumptions For valid results, multivariate regression assumes: Linearity between predictors and outcome Independence of residuals Homoscedasticity (constant variance of residuals) No or minimal multicollinearity among predictors Normally distributed residuals Thanks