Deck 15: Analysis of Variance

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A comparison of the treatments based on independently selected experimental units that are randomly assigned to treatments is referred to as a completely randomized design.
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The fundamental identity for a single-factor ANOVA is MSTo = MSTr + MSE
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SSE measures the within-sample variability.
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The P-value associated with the calculated F value in an ANOVA is the area to the right of the calculated value under an F distribution curve.
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As long as the other assumptions are plausible, the F test for testing the equality of population means in an ANOVA can be safely used when the largest sample standard deviation is no more than twice the smallest standard deviation.
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MSTr and MSE are statistics.
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SSE measures the within-sample variability.
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The P-value associated with the calculated F value in an ANOVA is the area to the right of the calculated value under an F distribution curve.
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The objective of a single-factor analysis of variance for k populations is to test the equality of the k population variances.
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Deck 15: Analysis of Variance
1
A comparison of the treatments based on independently selected experimental units that are randomly assigned to treatments is referred to as a completely randomized design.
True
2
The fundamental identity for a single-factor ANOVA is MSTo = MSTr + MSE
False
3
SSE measures the within-sample variability.
True
4
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5
The P-value associated with the calculated F value in an ANOVA is the area to the right of the calculated value under an F distribution curve.
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6
As long as the other assumptions are plausible, the F test for testing the equality of population means in an ANOVA can be safely used when the largest sample standard deviation is no more than twice the smallest standard deviation.
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7
MSTr and MSE are statistics.
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8
SSE measures the within-sample variability.
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9
The P-value associated with the calculated F value in an ANOVA is the area to the right of the calculated value under an F distribution curve.
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10
The objective of a single-factor analysis of variance for k populations is to test the equality of the k population variances.
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11
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12
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