MLR (Multiple Linear Regression)

Information

• MLR (Multiple Linear Regression) is a statistical technique that models the relationship between a dependent variable and multiple independent variables.
• It assumes that the relationship between the dependent variable and each independent variable is linear.
• The goal of MLR is to find a linear equation that best fits the observed data by minimizing the sum of squared errors between the predicted and actual values.
• The equation takes the form of Y = b0 + b1X1 + b2X2 + ... + bn*Xn, where Y is the dependent variable, X1, X2, ... Xn are the independent variables, b0 is the intercept, and b1, b2, ... bn are the coefficients.
• The coefficients represent the change in the dependent variable associated with a one-unit change in the corresponding independent variable, holding all other independent variables constant.
• MLR can be used for prediction, explanation, and hypothesis testing, and it requires the assumptions of linearity, independence, homoscedasticity, and normality of errors to be satisfied.
• MLR can be extended to include interaction terms, polynomial terms, and categorical variables, and it can be evaluated using metrics such as R-squared, adjusted R-squared, and mean squared error.