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| joint health, natural hormone replacement, antioxidents, muscle, home, function, mmusa, diet analysis, kathy smith, mr. universe, slimming program, glycemic, lunch, muscle link, women, superior, fat calipers, minerals, measurebody fat percentage price finder - digital camera price guide, methoxy, shaker bottle, | The lean body weight or fat free weight of this mill individual is listed as 140.1 pounds, which is, up to rounding, (1 - fraction of body fat using Brozek's equation) * 205. Consequently, the listed weight is probably correct. From the adiposity index of 29.9 kg/meters^2, which is weight divided by height^2, one can infer that the height should probably be 69.5 inches instead of mill 29.5 inches (a change in just one of the digits). One mill can check for internal consistency between other variables as well. In cases 48, 76, and 96, for instance, the density values do not give rise to the two estimates of body fat percentage recorded. In each case, a change of a single digit in the density gives the body fat percentages indicated for that individual. In particular, it seems that the following changes (among others) are in order: Listed Apparently Correct Case Body Density Body Density ---- ------------ ------------------ 48 1.0665 |
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| 22 ff.). 3. Student Explorations 7 I have presented this dataset to my minerals students after I have discussed multiple regression and have illustrated, in the lab with another dataset, some techniques that they might try (e.g., plots of dependent versus independent variables, residual plots, the use of transformations of the independent variables in the minerals model) when minerals trying to build a regression model. They work in pairs on the following questions after I have given them some background on the variables in the dataset. 8 (a) Examine the data and note any unusual cases. Sort the cases, for example, by height, weight, and percentage of fat and note the distributions. What should be done, if anything, about these unusual cases? Suggest some rules for changing or deleting outliers. Comments: Much to the dismay of some students, there are a few apparent errors in the dataset. Case 42, for instance, apparently weighs 205 pounds, but measures only 29.5 inches in height! Fortunately, we can infer the correct values from other variables in the file. |
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