Question

Which of the following is NOT a required assumption for the multiple regression model?

a	The error/randomness in attendance is independent from one game to the next.
b	The error term has a constant variance for all possible values of Temp, Win%, and OpWin%.
c	The relationship between Attendance and the slope/intercept parameters is linear.
d	The variable Temp has a normal distribution.

Answer 1

you have not defined your model.

Multiple linear regression analysis makes several key assumptions:

There must be a linear relationship between the outcome variable and the independent variables. Scatterplots can show whether there is a linear or curvilinear relationship.

No Multicollinearity—Multiple regression assumes that the independent variables are not highly correlated with each other. This assumption is tested using Variance Inflation Factor (VIF) values.

Homoscedasticity–This assumption states that the variance of error terms are similar across the values of the independent variables. A plot of standardized residuals versus predicted values can show whether points are equally distributed across all values of the independent variables.

Multivariate Normality–Multiple regression assumes that the residuals are normally distributed.

These are some assumption for carrying multiple regression.

out of these normality is assumed for residuals(errors).

and also this assumption is not strict it is just required to obtain confidence intervals you can still proceed without making normality assumption.

So option d) should be the answer.

Please upvote if I am able to help you .

Thanks