Page 59 - Intermediate Statistics for Dummies

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Part I: Data Analysis and Model-Building Basics
females. This assessment requires a hypothesis test of two means (often-
times called a t-test for independent samples). I present more information
on this technique in Chapter 3.
When comparing the means of more than two groups, don’t simply look at all
the possible t-tests that you can do on the pairs of means, because you have
to control for an overall error rate in your analysis. Too many analyses can
result in errors — adding up to disaster. For example, if you conduct 100
hypothesis tests, each one with a 5 percent error rate, then 5 of those 100
tests give wrong results on average, just by chance.
If you want to compare the average wage in different regions of the country
(the East, the Midwest, the South, and the West, for example), this compari-
son requires a more sophisticated analysis, because you’re looking at four
groups rather than just two. The procedure you can use to compare more
than two means is called analysis of variance (ANOVA), and I discuss this
method in detail in Chapters 9 and 10.
Finding connections
Suppose you’re an avid golfer and you want to figure out how much time you
should spend on your putting game. The question is this: Is the number of
putts related to your total score? If the answer is yes, then spending time on
your putting game makes sense. If not, then you can slack off on it a bit. Both
of these variables are quantitative variables, and you’re looking for a connec-
tion between them. You collect data on 100 rounds of golf played by golfers
at your favorite course over a weekend. Table 2-2 shows the first few lines of
your data set.
Table 2-2 First Ten Golf Scores (ordered)
Number of Putts Total Score
23 76
27 80
28 80
29 80
30 80
29 82