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Suppose that we performed an estimate of the determinants of players' salaries for the 2018 NFL season. The R2 = 0.65 for the regression that included 3 explanatory variables. Imagine that we decided to add an additional explanatory variable (like the players' previous playoff experience). When we run the regression with the additional explanatory variable the R2 = 0.78 is for the regression. We can interpret this as %3D

Suppose that we performed an estimate of the determinants of players' salaries for the 2018 NFL season. The R2 = 0.65 for the regression that included 3 explanatory variables. Imagine that we decided to add an additional explanatory variable (like the players' previous playoff experience). When we run the regression with the additional explanatory variable the R2 = 0.78 is for the regression. We can interpret this as %3D

Algebra & Trigonometry with Analytic Geometry
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 91E
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Suppose that we performed an estimate of the determinants of players' salaries for the 2018 NFL season. The R? = 0.65 for the regression that included 3 explanatory variables. Imagine that we decided to add an additional explanatory variable (like the players' previous playoff experience). When we run the regression with the additional explanatory variable the R2 = 0.78 is for the regression. We can interpret this as the additional explanatory variable is significant at the 5% level. the additional explanatory variable has lowered the explained sum of squares measure in the regression. the additional explanatory variable is related to player salary and does belong in the regression. the additional explanatory variable is not related to player salary and does not belong in the regression.
Transcribed Image Text:Suppose that we performed an estimate of the determinants of players' salaries for the 2018 NFL season. The R? = 0.65 for the regression that included 3 explanatory variables. Imagine that we decided to add an additional explanatory variable (like the players' previous playoff experience). When we run the regression with the additional explanatory variable the R2 = 0.78 is for the regression. We can interpret this as the additional explanatory variable is significant at the 5% level. the additional explanatory variable has lowered the explained sum of squares measure in the regression. the additional explanatory variable is related to player salary and does belong in the regression. the additional explanatory variable is not related to player salary and does not belong in the regression.
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