Suppose that we examine the relationship between high school GPA and college GPA. We collect data from students at a local college and find that there is a strong, positive, linear association between the variables. The linear regression predicted college GPA = 1.07 + 0.62 * high school GPA. The standard error of the regression, se, was 0.374. What does this value of the standard error of the regression tell us?

The typical error between a predicted college GPA using this regression model and an actual college GPA for a given student will be about 0.374 grade points in size (absolute value).

Step-by-step explanation:

The linear regression line for College GPA based on High school GPA is:

College GPA = 1.07 + 0.62 High-school GPA

It is provided that the standard error of the regression line is,

The standard error of a regression line is the average distance between the predicted value and the regression equation.

It is the square root of the average squared deviations.

It is also known as the standard error of estimate.

The standard error of 0.374 implies that:

The typical error between a predicted college GPA using this regression model and an actual college GPA for a given student will be about 0.374 grade points in size (absolute value).

required rate of return = 0.166 = 16.6%

step-by-step explanation:

under the capital asset pricing model (capm), the required or expected rate of return for a particular security or asset is given using the formula:

required rate of return = risk-free rate + beta*(market risk premium)

we are given that;

risk-free rate = 4% = 0.04

beta = 1.4

market risk premium = 9% = 0.09

we simply plug these values into the above formula;

required rate of return = 0.04 + 1.4*(0.09)

required rate of return = 0.04 + 0.126

required rate of return = 0.166 = 16.6%