Equations-Z = (X � M) / SDSD2 = SS / NSS = Σ(X-M)2M = Σ(X)/N
For the Z-Scores, please use the back of your book.
11, 12, 13, 13, 14, 14, 14, 15, 15, 16, 17
1, 40, 51, 102, 105, 107, 152, 155, 200
4, 6, 6, 7, 7, 9, 10, 11
14, 17, 19, 21, 22, 23, 25, 30, 34, 36
17, 20, 20, 20, 25, 26, 27, 30, 35, 37, 40, 40, 40, 46
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The mean, because it is the average of the scores
Average deviation, because it shows (on average) how the scores are spread out from the mean
Variance, because it is describes the spread of the deviation scores
Standard deviation, because it is labeled in usable units
Variance, because it is the square root of standard deviation
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σ
σ2
SD
SD2
S
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78.2
70.2
75
111
66.5
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3.2
-.64
-4
-3.2
4
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.17
.85
.53
.21
.74
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John's score falls closer to Kayla's mean than does Kayla's score to John's mean
John's score falls closer to his mean than does Kayla's score to her mean
John's mean/SD proportion is larger than Kayla's mean/SD proportion
Kayla's score falls closer to her mean than does John's score to his mean
None of the above
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2.88
8.25
82.5
0
5.5
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A variable is a possible number or category that a value can have
A value is a particular person's score, whereas a variable is the characteristic being measured
The variable is the characteristic being measured, and a value is a possible number or category a score can have
A variable is a categorical variable, but a value is a numeric variable
A variable can vary, but a value has no range
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A categorical variable
A nominal variable
An equal-interval variable
An ordinal variable
An equal-interval ratio variable
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Kurtotic Skew
Right/Postive Skew
Ceiling Effect
Left/Postive Skew
Right/NegativeSkew
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Descriptive Statistics
Variability
The Z Distribution
Inferential Statistics
Probability
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Sample, Population
Group, Sample
Subjects, Population
Sample, Subjects
Population, Sample
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Random
Exclusive
Selective Convenience
Haphazard
Problematic
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Relative Frequency
Expected Frequency
Expected Relative Frequency
Frequency
Probability
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