Today in class we discussed about linear regression model with more than one predictor variable, known as multiple linear regression, aims to capture the relationship between a dependent variable and several independent predictor variables. In this model, the dependent variable is expressed as a linear combination of these predictors, with each predictor having its own coefficient that signifies the strength and direction of its impact on the dependent variable. The model allows us to assess how changes in each predictor, while holding others constant, influence the outcome. By estimating these coefficients through techniques like least squares, we can make predictions, understand the significance of each predictor, and assess the overall goodness of fit. Multiple linear regression is a valuable tool in fields like economics, science, and social sciences for uncovering complex relationships and making data-driven predictions.