Assumptions of the classical linear regression model

Multiple regression fits a linear model by relating the predictors to the target variable. The model has the following form:

Y = B0 + B1 * X1 + B2 * X2 + … + Bp * Xp + e

Here, Y is the target variable, the Xs are the predictors, and the e term is the random disturbance. The Bs are capitalized to indicate that the are population parameters. Estimates of the Bs are found from the sample such that the sum of squares of the sample errors is minimized. The term ordinary least squares regression captures this feature.

The assumptions of the classical linear regression model are as follows:

• The target variable can be calculated as a linear function of a specific set of predictor variables plus a disturbance term. The coefficients in this linear function are constant.
• The expected value of the disturbance term is zero.
• The disturbance...