Question
A student regressed Y against both X and X 2 and obtained the following output tables. Because X 2 is not significant at the 0.05
A student regressed Y against both X and X2 and obtained the following output tables.
Because X2 is not significant at the 0.05 level, the student reran the regression after dropping X2 and obtained the following output tables.
Why does dropping a variable that is not significant cause a big decrease in adjusted r squared?
Regression Statistics Multiple R 0.682335 R Square 0.465581 Adjusted F 0.406201 Standard 11.70702 Observati ANOVA Regression Residual Total df X X2 21 SS MS F gnificance F 2 2149.211 1074.605 7.840724 0.003556 18 2466.979 137.0544 20 4616.19 P-value Lower 95%Upper 95% Coefficientsandard Err t Stat Intercept 31.88669 3.839323 8.305289 1.43E-07 23.82057 39.9528 -1.49703 0.421892 -3.54838 0.002296 -2.38339 -0.61067 0.13741 0.078164 1.757973 0.095744 -0.02681 0.301626
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Based on the updated regression output tables after dropping X2 here is a breakdown of the key metrics 1 Multiple R The multiple correlation coefficient which measures the strength and direction of th...
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Statistics For Business And Economics
Authors: Paul Newbold, William Carlson, Betty Thorne
8th Edition
0132745658, 978-0132745659
