download.file("http://www.openintro.org/stat/data/bdims.RData", destfile = "bdims.RData")
load("bdims.RData")
mdims <- subset(bdims, sex == 1)
fdims <- subset(bdims, sex == 0)

Question 1

Now let’s consider some of the other variables in the body dimensions data set. Using the figures at the end of the exercises, match the histogram to its normal probability plot. All of the variables have been standardized (first subtract the mean, then divide by the standard deviation), so the units won’t be of any help. If you are uncertain based on these figures, generate the plots in R to check.

a. The histogram for female biiliac (pelvic) diameter (`bii.di`) belongs
to normal probability plot letter B.

b. The histogram for female elbow diameter (`elb.di`) belongs to normal 
probability plot letter C.

c. The histogram for general age (`age`) belongs to normal probability 
plot letter D.

d. The histogram for female chest depth (`che.de`) belongs to normal 
probability plot letter A.
histQQmatch

histQQmatch

#the eval=FALSE makes knitr show the code but it doesn't evaluate it in the html document.
#Show the code you used in order to match the qqplots here

qqnorm(fdims$bii.di)
qqline(fdims$bii.di)

Question 2

Note that normal probability plots C and D have a slight stepwise pattern. Why do you think this is the case?

When the data was reported, people likely only reported whole number values, as opposed to the x-axis, which is trying to create a continuous data set, and as a result creates a bit of a step pattern.

Question 3

As you can see, normal probability plots can be used both to assess normality and visualize skewness. Make a normal probability plot for female knee diameter (kne.di). Based on this normal probability plot, is this variable left skewed, symmetric, or right skewed? Use a histogram to confirm your findings.

qqnorm(fdims$kne.di)
qqline(fdims$kne.di)

hist(fdims$kne.di)

Looking at the probability plot it’s rather right-skewed, and this can also be shown in the histogram.