Question

1. A multiple linear regression model should not be used if:
A The variables are all statistically significant.
B The coefficient of determination R2 is large.
C Both of the above.
D Neither of the above.

2. Consider a multiple linear regression model where the output variable is a company's revenue for
different months, and the purpose is to investigate how the revenue depends upon the company's advertising budget. The input variables can be time-lagged so that the first input variable is the advertising budget in that month, the second input variable is the advertising budget in the previous month, etc.
A True.
B False.

Answer 1

1. A multiple linear regression model should not be used if

multiple linear regression requires the relationship between the independent and dependent variables to be linear. The linearity assumption can best be tested with scatterplots.

in multiple regression predictors with small coefficients of variation that are nearly constant can cause numerical problems

if the coefficient of variation is small, some loss of statistical accuracy will occur

It is difficult to use when the coefficient of determination R2 is small

Hence option D is correct

2.

the given statement is a multiple linear regression model where the output variable is a company's revenue for different months, and the purpose is to investigate how the revenue depends upon the company's advertising budget. The input variables can be time-lagged so that the first input variable is the advertising budget in that month, the second input variable is the advertising budget in the previous month

The statement explains that comapanys revenue for severaal months. The investigator studies how advertising budget affects the revenue.

To utilize this information a simple linear regression was performend.

So this data is based on the time factor the inputvariable can be lagged

Hene this statement is true