Introduction to Statistics (MAT/SST 115.03 2008S)

R notes for Activity 20-1: Basketball Scoring

You can load the sample NBA points data with

nba = read.csv("/home/rebelsky/Stats115/Data/NBAPoints.csv")

You will note that there are two columns in the data frame: One for the date in which the game was played and one for the total score that night.

> head(nba)
        Date Points
1 12/10/1999    196
2 12/10/1999    198
3 12/10/1999    205
4 12/10/1999    163
5 12/10/1999    184
6 12/10/1999    224

20-1 c. Visual Displays

It's been awhile, so you may not remember the techniques we use for visual displays of quantitative data. Let's review. You can try

  • a dot plot,
    library(BHH2, lib="/home/rebelsky/Stats115/Packages")
  • a histogram,
  • or a normal probability plot.
    qqnorm(nba$Points, datax=T)
    qqline(nba$Points, datax=T)

20-1 d. Descriptive Statistics

A quick reminder ...


20-1 i. The t-test

You should compute the t-test statistic using the formula on p. 395. Note that you'll need to parenthesize the denominator.

(x_bar - mu_0)/(s/sqrt(n))

20-1 l. Using Technology

After using the applet, you might also just want to have R compute the test statistic and p-value directly from the original data.

t.test(nba$Points, mu=183.2, alternative="greater")

Sometimes you just have the test statistic, and not the original data (or just the mean and standard deviation, and not the original data, in which case you can compute the test statistic). In that case, you can still have R compute the p-value using the pt function. You need to supply the test statistic and the degrees of freedom. As is the case for the standard normal probability table, this gives the area to the left, so you will need to subtract it from 1.

pt(3.13, df=24)

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Samuel A. Rebelsky,

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

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