The predictor variable is indicated as highly correlated with the response variable (Independent variable). However, these variables are not highly correlated with the other predictor variables. Hence, this is the characteristics of a good predictor variable.
The four regression model assumptions are as follows:
• Zero mean assumption
• Constant variance assumption
• Independence assumption
• Normality assumption
Zero Mean Assumption:
The mean of the residual values is zero. That is, the residuals are independent with each other.
Constant variance assumption:
The variance of the values of independent variables is constant.
There is a linear relation between the response variable and each predictor variable.
The residual values are distributed as normal in the formation of the regression equation.
The partial, or net regression coefficient is used to measure the average change (increase or decrease) in the response variable as per unit changes (increase or decrease) in the corresponding predictor variable, considering other predictor variables as constant.