example_MR1 - Stat 2225 Multiple Regression Example #1...

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Stat 2225 Multiple Regression Example #1 There were 252 men selected to see how percent body fat depends on the various measurements from the body. In this example, we’re interested in: Response: body percent fat (in %); (Independent) Variables: (1) abdomen circumference (in cm), (2) chest circumference (in cm), (3) knee circumference (in cm) and (4) weight (in lbs). Multiple Regression Analysis ----------------------------------------------------------------------------- Dependent variable: Percent ----------------------------------------------------------------------------- Standard T Parameter Estimate Error Statistic P-Value ----------------------------------------------------------------------------- CONSTANT -50.6177 8.10827 -6.24272 0.0000 Abdomen 0.988893 0.0705948 14.008 0.0000 Chest 0.00907003 0.09405 0.0964384 0.9233 Knee 0.160688 0.228307 0.703827 0.4822 Weight -0.161381 0.0302167 -5.34077 0.0000 ----------------------------------------------------------------------------- Analysis of Variance ----------------------------------------------------------------------------- Source Sum of Squares Df Mean Square F-Ratio P-Value ----------------------------------------------------------------------------- Model 12645.6 4 3161.41 158.28 0.0000 Residual 4933.34 247 19.973 ----------------------------------------------------------------------------- Total (Corr.) 17579.0 251 R-squared = 71.9362 percent R-squared (adjusted for d.f.) = 71.4817 percent Standard Error of Est. = 4.46912 Residual Plot predicted Percent Studentized residual 0 1 02 03 04 05 0 -3 -2 -1 0 1 2 3 Page 1 of 2
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Histogram SRESIDUALS frequency - 3- 2- 10123 0 10 20 30 40 50 Normal Probability Plot SRESIDUALS percentage - 3 - 2 - 0.1 1 5 20 50 80 95 99 99.9 Correlation matrix for coefficient estimates -------------------------------------------------------------------------------------- CONSTANT Abdomen Chest Knee
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example_MR1 - Stat 2225 Multiple Regression Example #1...

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