dsci test 2.docx - 11.1-11.4 12.1-12.8 A simple way to...

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11.1-11.4, 12.1-12.8 A simple way to state the one-factor ANOVA hypothesis is H 0 : μ 1 = μ 2 = μ 3 = μ 4 (mean defect rates are the same at all four plants) H 1 : Not all the means are equal (at least one mean differs from the others) Analysis of variance assumes that the Observations on Y are independent. Populations being sampled are normal. Populations being sampled have equal variances. ANOVA calculations usually are too tedious to do by calculator, so after we choose an ANOVA model and collect the data, we rely on software. Large samples increase the power of the test, but power also depends on the degree of variation in Y. Specialized software is needed to calculate power for ANOVA experiments. 11.2 If we are only interested in comparing the means of c groups (treatments or factor levels), we have a one- factor ANOVA. If subjects (or individuals) are assigned randomly to treatments, then we call this the completely randomized model. One factor ANOVA as a Linear model: Group means Hartley's test , named for statistician H. O. Hartley (1912–1980). The hypotheses are
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