Multiple regression uses the same process employed in simple regression for predicting the dependent variable’s value. It, however, does so with more items summed up, as shown below: $$ \widehat{Y_f}={\hat{b}}_0+{\hat{b}}_1X_{1f}+{\hat{b}}_2X_{2f}+\ldots+{\hat{b}}_kX_{kf}={\hat{b}}_0+\sum_{j=1}^{k}{{\hat{b}}_jX_{jf}} $$ Where: \({\hat{Y}}_f\) = Predicted (forecasted) value of the dependent…

In multiple regression, the intercept in simple regression represents the expected value of the dependent variable when the independent variable is zero, while in multiple regression, it’s the expected value when all independent variables are zero. The interpretation of slope…

The following assumptions are used to build multiple regression models: The relationship between the dependent variable, and the independent variables, is linear. The independent variables are not random. There is no definite linear relationship between two or more independent variables….

R-squared \(\bf{(R^2)}\) measures how well an estimated regression fits the data. It is also known as the coefficient of determination and can be formulated as: $$ R^2=\frac{\text{Sum of regression squares}}{\text{Sum of squares total}}=\frac{{\sum_{i=1}^{n}{(\widehat{Y_i}-\bar{Y})}}^2}{{\sum_{i=1}^{n}{(Y_i-\bar{Y})}}^2} $$ Where: \(n\) = Number of observations….

Example: Multiple Regression in Investment World James Chase, an investment analyst, wants to determine the impact of inflation rates and real rates of interest on the price of the US Dollar index (USDX). Chase uses the multiple regression model below:…

Multiple linear regression describes the variation of the dependent variable by using two or more independent variables. When used properly, it can improve predictions. However, if used incorrectly, it can create spurious relationships that can undermine predictions. Typically, a multiple…