Introduction to Statistics (MAT/SST 115.03 2008S)

R notes for Activity 9-3: Value of Statistics


Although the book tells you that the data are stored in a single file, I've found it easier to segment it into five files, ClassF, ClassG, ClassH, ClassI, and ClassJ. You should load each separately. For example,

ClassF = read.csv("/home/rebelsky/Stats115/Data/ClassF.csv")
ClassG = read.csv("/home/rebelsky/Stats115/Data/ClassG.csv")
ClassH = read.csv("/home/rebelsky/Stats115/Data/ClassH.csv")
ClassI = read.csv("/home/rebelsky/Stats115/Data/ClassI.csv")
ClassJ = read.csv("/home/rebelsky/Stats115/Data/ClassJ.csv")

Each of these CSV files contains a single column, titled Ratings. Hence, to make a histogram for one of them, you would write something like the following.

hist(ClassF$Ratings)

Of course, the book doesn't tell you to make your own histograms, but you might find it useful to do so.

What the book does is ask you to compute a variety of numbers, including range, interquartile range, and standard deviation. R's range function gives you the min and the max, rather the difference between the two. To compute the difference between the two, you need to subtract the max from the min.

max(ClassF$Ratings) - min(ClassF$Ratings)

You compute interquartile range with IQR and standard deviation with sd. (And no, I do not know why they use different capitalization in different places.)

IQR(ClassF$Ratings)
sd(ClassF$Ratings)

Problems i and j ask you to create a hypothetical example. Use something like the following (replacing the 0's by other numbers).

iHypotheticals = c(0,0,0,0,0,0,0,0,0,0)
sd(iHypotheticals)
jHypotheticals = c(0,0,0,0,0,0,0,0,0,0)
sd(jHypotheticals)

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Samuel A. Rebelsky, rebelsky@grinnell.edu

Copyright (c) 2007-8 Samuel A. Rebelsky.

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