A data set containing many physical measurements of 252 males. Most of the variables can be measured with a scale or tape measure. Can they be used to predict the percentage of body fat? If so, this offers an easy alternative to an underwater weighing technique.
A data frame with 252 observations on the following 19 variables.
Percent body fat using Brozek's equation, 457/Density - 414.2
Percent body fat using Siri's equation, 495/Density - 450
Adiposity index = Weight/Height^2 (kg/m^2)
Fat Free Weight = (1 - fraction of body fat) * Weight, using Brozek's formula (lbs)
Neck circumference (cm)
Chest circumference (cm)
Abdomen circumference (cm) "at the umbilicus and level with the iliac crest"
Hip circumference (cm)
Thigh circumference (cm)
Knee circumference (cm)
Ankle circumference (cm)
Extended biceps circumference (cm)
Forearm circumference (cm)
Wrist circumference (cm) "distal to the styloid processes"
From the source:
“The data are as received from Dr. Fisher. Note, however, that there are a few errors. The body densities for cases 48, 76, and 96, for instance, each seem to have one digit in error as can be seen from the two body fat percentage values. Also note the presence of a man (case 42) over 200 pounds in weight who is less than 3 feet tall (the height should presumably be 69.5 inches, not 29.5 inches)! The percent body fat estimates are truncated to zero when negative (case 182).”
This data set comes from the collection of the Journal of Statistics Education at http://www.amstat.org/publications/jse/datasets/fat.txt. The data set was contributed by Roger W. Johnson.
The source of the data is attributed to Dr. A. Garth Fisher, Human Performance Research Center, Brigham Young University, Provo, Utah 84602,
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Loading required package: MASS Loading required package: HistData Loading required package: Hmisc Loading required package: lattice Loading required package: survival Loading required package: Formula Loading required package: ggplot2 Attaching package: 'Hmisc' The following objects are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units Attaching package: 'UsingR' The following object is masked from 'package:survival': cancer Call: lm(formula = f, data = fat) Residuals: Min 1Q Median 3Q Max -10.2573 -2.5919 -0.1031 2.9040 9.2754 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.519e+01 1.611e+01 -0.943 0.3467 age 5.688e-02 3.003e-02 1.894 0.0594 . weight -8.130e-02 4.989e-02 -1.630 0.1045 height -5.307e-02 1.034e-01 -0.513 0.6084 BMI 6.101e-02 2.780e-01 0.219 0.8265 neck -4.450e-01 2.184e-01 -2.037 0.0427 * chest -3.087e-02 9.779e-02 -0.316 0.7526 abdomen 8.790e-01 8.545e-02 10.286 <2e-16 *** hip -2.031e-01 1.371e-01 -1.481 0.1398 thigh 2.274e-01 1.356e-01 1.677 0.0948 . knee -9.927e-04 2.298e-01 -0.004 0.9966 ankle 1.572e-01 2.076e-01 0.757 0.4496 bicep 1.485e-01 1.600e-01 0.928 0.3543 forearm 4.297e-01 1.849e-01 2.324 0.0210 * wrist -1.479e+00 4.967e-01 -2.978 0.0032 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.996 on 237 degrees of freedom Multiple R-squared: 0.749, Adjusted R-squared: 0.7342 F-statistic: 50.52 on 14 and 237 DF, p-value: < 2.2e-16
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