1. Compute the Adjusted R-Squared value for Model 1. Discuss why Adjusted R-squared and the R-squared values are different.

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Model 1 Let's consider the following R output for a regression model which we will refer to as Model 1. (Note 1: In the ANOVA table, I have added 2 rows - (1) Model DF and Model SS - which is the sum of the rows corresponding to all the 4 variables (2) Total DF and Total SS - which is the sum of all the rows; Note 2: The F test corresponding to the Model denotes the overall significance test. In R output, you will see that at the bottom of the Coefficients table) ANOVA: Df Sum Sq Mean Sq F value Pr(>F) X1 X2 X3 X4 Residuals 1 1974.53 1974.53 209.8340 <0.0001 1 118.8642568 118.8642568 12.6339 1 32.47012585 32.47012585 3.4512 0.0007 0.0676 1 0.435606985 0.435606985 0.0463 0.8303 67 630.36 9.41 Note: You can make the following calculations from the ANOVA table above to get Overall F statistic Model (adding 4 rows) Total (adding all rows) 4 71 2126 2756.37 531.50 <0.0001 Coefficients: Estimate Std. Error t value Pr(>t) Intercept 11.3303 1.9941 5.68 Model 1 Let's consider the following R output for a regression model which we will refer to as Model 1. (Note 1: In the ANOVA table, I have added 2 rows - (1) Model DF and Model SS - which is the sum of the rows corresponding to all the 4 variables (2) Total DF and Total SS - which is the sum of all the rows; Note 2: The F test corresponding to the Model denotes the overall significance test. In R output, you will see that at the bottom of the Coefficients table) ANOVA: Df Sum Sq Mean Sq F value Pr(>F) X1 X2 X3 X4 Residuals 1 1974.53 1974.53 209.8340 <0.0001 1 118.8642568 118.8642568 12.6339 1 32.47012585 32.47012585 3.4512 0.0007 0.0676 1 0.435606985 0.435606985 0.0463 0.8303 67 630.36 9.41 Note: You can make the following calculations from the ANOVA table above to get Overall F statistic Model (adding 4 rows) Total (adding all rows) 4 71 2126 2756.37 531.50 <0.0001 Coefficients: Estimate Std. Error t value Pr(>t) Intercept 11.3303 1.9941 5.68