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(Fitting Percentage of Body Fat to Simple Body Measurements) Per- centage of body fat, age, weigh...

  1. (Fitting Percentage of Body Fat to Simple Body Measurements) Per- centage of body fat, age, weight, height, and ten body circumference measurements (e.g., abdomen) are recorded for 252 men. The following two links provide a description to the dataset and the list of the data values,

      http://jse.amstat.org/datasets/fat.txt
      http://jse.amstat.org/datasets/fat.dat.txt
    

    The R function read.table() can then be used to read the data values
    into R from the given link,
    fat < − read.table(”http://jse.amstat.org/datasets/fat.dat.txt”,header=FALSE) You can then use the function colnames() to name the variables (columns), colnames(fact) < − c(”ID”,’pctfat.brozek’, ... , ’age’, ’weight’, ’height’,
    ...)
    (a) Use the function plot(x,y) in R to obtain four scatterplots: ”Per-
    cent body fat using Brozek’s equation” versus ”Neck circumference”,
    ”Percent body fat using Brozek’s equation” versus ”Chest circumfer-
    ence” , ”Percent body fat using Brozek’s equation” versus ”Weight”,
    and ”Percent body fat using Brozek’s equation” versus ”Height”. Use par(mfrow=c(2,2)) in R to combine the four plots into one overall
    graph. Then compute, using R function cor(x,y), the four correlation coefficients. Which variable, among Neck circumference, Chest circum-
    ference, Weight, and Height has the strongest linear association with
    Percent body fat using Brozek’s equation?
    (b) Use Fisher’s transformation to test the hypothesis that theoreti-
    cally ”height” and ”pctfat.brozek” have zero correlation; i.e., rho = 0.
    Read the section on Fisher’s transformation of correlation in the lecture
    notes of 03/19 and then follow the examples.

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Two ways to increase the statistical power of an experiment overlap in the two sampling distributions for null hypothesis and

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