Financial Risk Manager Part 1

Get started today

Ultimate access to all questions.

Explanation:

$R^2$ always increases with the number of explanatory variables in a regression equation; however, adjusted $R^2$ may not since there is a penalty function associated with adding variables that do not provide additional explanatory power.

(Book 2, Module 19.2, LO 19.c)

Explanation:

(Book 2, Module 19.2, LO 19.c)

Comments (0)

No comments yet.

Question 79

"Determining which variables to include in a regression is difficult," mentioned a financial analyst at Investors International. "No, it's not," replied his supervisor. "All you have to do is maximize $R^2$ . Just keep adding explanatory variables until $R^2$ does not change, and there you go." The analyst decided to investigate this further and was surprised to find information on adjusted $R^2$ . Which of the following best describes $R^2$ and adjusted $R^2$ when it comes to the number of explanatory variables in a regression?

Other

Community

UAnonymous

Last updated: July 3, 2026 at 10:47

$R^2$ and adjusted $R^2$ increase with the number of explanatory variables.

$R^2$ and adjusted $R^2$ decrease with the number of explanatory variables.

$R^2$ increases, but adjusted $R^2$ may not with the number of explanatory variables.

$R^2$ decreases, but adjusted $R^2$ increases with the number of explanatory variables.