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Analysis of variance (ANOVA) |
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Definition
An inferential method used to test the equality of three or more population means |
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1. A comparison of two estimates of the same population variance 2. One-way because there is only one factor that distinguishes the various populations |
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4 requirements of a one-way ANOVA test |
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1. There are k simple random variables; one from each of k populations 2. The k samples are independent of each other 3. The populations are normally distributed 4. The populations have the same variance, sigma^2 |
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The methods of one-way ANOVA (are/are not) robust |
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The methods of one-way ANOVA are robust |
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The one-way ANOVA procedures may used provided that |
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The largest sample standard deviation is no more than twice the smallest sample standard deviation |
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Between-sample variability |
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The variability among the sample means |
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Within-sample variability |
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The variability of each sample |
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ANOVA f-test statistic, F0 = |
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Between-sample variability/within-sample variability |
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A sum of squares divided by the corresponding degrees of freedom |
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Summation(xi - xbar)^2/n-1 |
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The variability of the data about the sample mean |
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The F-statistic is the ratio of |
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The between-sample variability and the within-sample variability |
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Mean square due to error (MSE) = |
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Sum of squares due to error (SSE) = |
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The MSE is an unbiased estimator of |
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Sigma^2 regardless of H0 and H1 |
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Mean square due to treatment (MST) is |
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The between-sample variability estimate of sigma^2 (numerator of the F-statistic) |
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Sum of squares due to treatment (SST) = |
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Summation[nk(xbark - xbar)^2] |
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MST is an unbiased estimator of |
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Sigma^2 only if the null is true |
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If F-test statistic is large, it is evidence |
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Definition
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1. Computing the F-test statistic |
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Compute sample mean, xbar |
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2. Computing the F-test statistic |
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Find sample mean for each sample, x1bar and x2bar |
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3. Computing the F-test statistic |
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Find sample variance for each sample, s1^2 and s2^2 |
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4. Computing the F-test statistic |
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5. Computing the F-test statistic |
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6. Computing the F-test statistic |
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