Basic Statistics for Business and Economics ch 12
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| Analysis of Variance (ANOVA) | The simultaneous comparison of several population means.
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| What are the characteristics of the F distribution? | There is a family. It is continuous. It cannot be negative. It is positively skewed. It is asymptotic.
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| The F distribution is used to test: | The hypothesis that the variance of one normal population equals the variance of another normal population.
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| To use the ANOVA, we assume the following: | The populations follow the normal distribution. The populations have equal standard deviation (sigma). The populations are independent.
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| Total Variation | The sum of the squared differences between each observation and the overall mean.
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| Treatment Variation | The sum of the squared differences between each treatment mean and the grand or overall mean.
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| Random Variation | The sum of the squared differences between each observation and its treatment mean.
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| SS Total (Sum of Squares Total) = | SST + SSE
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| SST | Sum of Squares Treatment
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| SSE | Sum of Squares Error
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| MSE (Mean Square Error) = | SSE/(n-k)
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| MST (Mean Square Treatment) = | SST/(k-1)
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| F = | MST/MSE
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| Degrees of freedom in the numerator = | k - 1
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| Degrees of freedom in the denominator = | n - k
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| k = | Number of Treatments
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| n = | Number of Observations
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