Behavioral Sciences STAT

Mathematics

Quiz 4 :Summarizing Scores With Measures of Variability

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Measures of variability are used to
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Multiple Choice

A

The term variability is most opposite to
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B

The greater the variability in a set of scores,
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A

A program evaluator for a large school district wants to know how much intelligence varies in the elementary schools.She selects one classroom at each grade level in each elementary school and administers an intelligence test to the children in those classrooms.In order to find out which school has the most consistent intelligence level, which of the following should she calculate for each school?
Multiple Choice
A program evaluator for a large school district wants to know how much intelligence varies in the elementary schools.She selects one classroom at each grade level in each elementary school and administers an intelligence test to the children in those classrooms.If the program evaluator wants to find out which school has the highest intelligence level, which of the following should she calculate for each school?
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As with measures of centrality, the selection of a measure of variability should be based on
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When computing the variance, why do we square the deviations from the mean?
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Variance is defined as the
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If the variance for a sample is computed and it is found to be rather large, the scores
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Standard deviation is defined as the square root of the
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If a sample has a small standard deviation, we can say the scores in the sample are
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In general, the larger the value of the standard deviation,
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The greater the variability, the more spread out the scores are around the
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The variance can never be
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Suppose we compute $S_{X}$ and find it is equal to 5.5.How do we interpret this number?
Multiple Choice
If you see the notation $\sum X^{2}$ , you should
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If you see the notation $(\Sigma X)^{2}$ , you should
What is wrong with the following formula? $S_{X}^{2}=\sqrt{\frac{\sum X^{2}-\frac{(\Sigma X)^{2}}{N}}{N}}$
What do $\left(S_{X}^{2}\right)$ , $\left(s_{X}^{2}\right)$ , and $\left(\sigma_{X}^{2}\right)$ have in common?