Transcription of Multiple Regression - Statistics at UC Berkeley
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Multiple Regression - Statistics at UC Berkeley
29. C H A P T E R. Multiple Regression I. n Chapter 27 we tried to predict the percent body fat of male subjects from WHO 250 Male subjects their waist size, and we did pretty well. The R2 of says that we ac- W H AT Body fat and waist counted for almost 68% of the variability in %body fat by knowing only the size waist size. We completed the analysis by performing hypothesis tests on the coef- UNITS %Body fat and inches ficients and looking at the residuals. WHEN 1990s But that remaining 32% of the variance has been bugging us. Couldn't we do a WHERE United States better job of accounting for %body fat if we weren't limited to a single predictor? WHY Scientific research In the full data set there were 15 other measurements on the 250 men. We might be able to use other predictor variables to help us account for that leftover varia- tion that wasn't accounted for by waist size. What about height? Does height help to predict %body fat?
ably enough, we can still solve this problem. Even better, a statistics package can find the coefficients of the least squares model easily. Here’s a typical example of a multiple regression table: Dependent variable is: Pct BF R-squared 5 71.3% R-squared (adjusted) 5 71.1% s 5 4.460 with 250 2 3 5 247 degrees of freedom
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