SCENARIO 14-8
A financial analyst wanted to examine the relationship between salary (in $1,000) and 2 variables: age (X₁ = Age) and experience in the field (X₂ = Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output:
$SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000) and 2 variables: age (X1 = Age) and experience in the field (X2 = Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output: Also, the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the sum of squares due to the regression for the model that includes only Exper is 125.9848. -Referring to Scenario 14-8,the analyst wants to use a t test to test for the significance of the coefficient of X2.At a level of significance of 0.01,the department head would decide that \beta 2 \neq 0 .$
$SCENARIO 14-8 A financial analyst wanted to examine the relationship between salary (in $1,000) and 2 variables: age (X1 = Age) and experience in the field (X2 = Exper). He took a sample of 20 employees and obtained the following Microsoft Excel output: Also, the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the sum of squares due to the regression for the model that includes only Exper is 125.9848. -Referring to Scenario 14-8,the analyst wants to use a t test to test for the significance of the coefficient of X2.At a level of significance of 0.01,the department head would decide that \beta 2 \neq 0 .$
Also, the sum of squares due to the regression for the model that includes only Age is 5022.0654 while the sum of squares due to the regression for the model that includes only Exper is 125.9848.
-Referring to Scenario 14-8,the analyst wants to use a t test to test for the significance of the coefficient of X₂.At a level of significance of 0.01,the department head would decide that

\beta

₂

\neq

0 .

An Interaction Term in a Multiple Regression Model May Be

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