Lecture 4 2023-08-24 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 ) 1. The histogram for female biiliac (pelvic) diameter ( bii.di ) belongs to normal probability plot letter B. 2. The histogram for female elbow diameter ( elb.di ) belongs to normal probability plot letter C. 3. The histogram for general age ( age ) belongs to normal probability plot letter D. 4. The histogram for female chest depth ( che.de ) belongs to normal probability plot letter A. #A qqnorm (fdims $ che.de) qqline (fdims $ che.de)
#B qqnorm (fdims $ bii.di) qqline (fdims $ bii.di) #C qqnorm (fdims $ elb.di) qqline (fdims $ elb.di)
#D qqnorm (fdims $ age) qqline (fdims $ age) Note that normal probability plots C and D have a slight stepwise pattern. Why do you think this is the case? -> Stepwise pattern is common among discrete data. In this case, Age is a whole number, which normally falls in the range of 0-100. Elbow diameter is rounded to 1 decimal, and the differences between each sample is small. 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. -> It's right skewed. qqnorm (fdims $ kne.di) qqline (fdims $ kne.di)
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