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Statistics for the Behavioral Sciences by Frederick J. Gravetter, Larry B. Wallnau ISBN 10: 1305504917 ISBN 13: 9781305504912

Statistics is one of the most practical and essential courses that you will take, and a primary goal of this popular text is to make the task of learning statistics as simple as possible. Straightforward instruction, built-in learning aids, and real-world examples have made STATISTICS FOR THE BEHAVIORAL SCIENCES, 10th Edition the text selected most often by instructors for their students in the behavioral and social sciences. The authors provide a conceptual context that makes it easier to learn formulas and procedures, explaining why procedures were developed and when they should be used. This text will also instill the basic principles of objectivity and logic that are essential for science and valuable in everyday life, making it a useful reference long after you complete the course.

Statistics is one of the most practical and essential courses that you will take, and a primary goal of this popular text is to make the task of learning statistics as simple as possible. Straightforward instruction, built-in learning aids, and real-world examples have made STATISTICS FOR THE BEHAVIORAL SCIENCES, 10th Edition the text selected most often by instructors for their students in the behavioral and social sciences. The authors provide a conceptual context that makes it easier to learn formulas and procedures, explaining why procedures were developed and when they should be used. This text will also instill the basic principles of objectivity and logic that are essential for science and valuable in everyday life, making it a useful reference long after you complete the course.

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SECTION 5.4 | Using z-Scores to Standardize a Distribution 143

■ Demonstration of a z-Score Transformation

Although the basic characteristics of a z-score distribution have been explained logically,

the following example provides a concrete demonstration that a z-score transformation

creates a new distribution with a mean of zero, a standard deviation of 1, and the same

shape as the original population.

EXAMPLE 5.8

We begin with a population of N = 6 scores consisting of the following values: 0, 6, 5, 2, 3, 2.

This population has a mean of μ = 18

6 = 3 and a standard deviation of σ = 2 (check the

calculations for yourself).

Each of the X values in the original population is then transformed into a z-score as

summarized in the following table.

X = 0

X = 6

X = 5

X = 2

X = 3

X = 2

Below the mean by 1 1 2 standard deviations z = −1.50

Above the mean by 1 1 2 standard deviations z = +1.50

Above the mean by 1 standard deviation z = +1.00

Below the mean by 1 2 standard deviation z = −0.50

Exactly equal to the mean—zero deviation z = 0

Below the mean by 1 2 standard deviation z = −0.50

The frequency distribution for the original population of X values is shown in Figure 5.8(a)

and the corresponding distribution for the z-scores is shown in Figure 5.8(b). A simple comparison

of the two distributions demonstrates the results of a z-score transformation.

1. The two distributions have exactly the same shape. Each individual has exactly the

same relative position in the X distribution and in the z-score distribution.

2. After the transformation to z-scores, the mean of the distribution becomes μ = 0.

For these z-scores values, N = 6 and Σz = −1.50 + 1.50 + 1.00 + −0.50 + 0 +

−0.50 = 0. Thus, the mean for the z-scores is μ = Σz/N = 0/6 = 0.

(a)

Frequency

2

1

s

0

1 2 3

m

4

5 6

X

(b)

F I G U R E 5.8

Transforming a

distribution of raw

scores (a) into

z-scores (b) does not

change the shape of

the distribution.

Frequency

2

1

–1.5

–1.0 –0.5 0

m

s

+0.5

+1.0 +1.5

z

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