Assumptions of Multiple Regression Model:

Linearity:
- The relationship between the dependent variable and independent variables should be linear.
- This assumption ensures that the changes in the independent variables lead to proportional changes in the dependent variable.

Independence of Errors:
- The errors or residuals should be independent of each other.
- This assumption implies that there should be no correlation between the errors in the regression model.

Homoscedasticity:
- The variance of the errors should be constant across all levels of the independent variables.
- This assumption ensures that the spread of the residuals is consistent.

Normality of Errors:
- The errors of the regression model should be normally distributed.
- This assumption is important for hypothesis testing and constructing confidence intervals.

No Multicollinearity:
- The independent variables should not be highly correlated with each other.
- Multicollinearity can lead to unreliable estimates of the regression coefficients.

No Autocorrelation:
- The errors should not be correlated with each other.
- Autocorrelation can lead to biased and inefficient estimates of the regression coefficients.
Ensuring that these assumptions hold true is crucial for the validity of the multiple regression model and the interpretation of its results. Violations of these assumptions can lead to biased estimates and incorrect conclusions.