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Chi-square distribution, chi^2 = |
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(n-1)s^2/sigma^2 where s^2 is a sample variance has a chi-square distribution with n-1 degrees of freedom |
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1. Characteristics of chi-square distribution |
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2. Characteristics of chi-square distribution |
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The shape of the chi-square distribution depends on the degrees of freedom |
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3. Characteristics of chi-square distribution |
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As the number of degrees of freedom increases, the chi-square distribution becomes more nearly symmetric |
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4. Characteristics of chi-square distribution |
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The values of chi^2 are nonnegative; greater than or equal to 0 |
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2 assumptions for testing hypotheses about a population variance or sd |
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1. The sample is obtained using simple random sampling 2. The population is normally distributed |
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H0: sigma = sigma0 H1: sigma not=to sigma0 |
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H0: sigma = sigma0 H1: sigma < sigma0 |
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H0: sigma = sigma0 H1: sigma > sigma0 |
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Two-tailed test, classical approach If chi0^2 > chi^2(alpha/2), then |
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Left-tailed test, classical approach If chi0^2 < chi^2(1-alpha/2), then |
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Right-tailed test, classical approach If chi0^2 > chi^2(alpha/2), then |
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P-value approach If p-value < alpha, then |
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P-value approach If p-value > alpha, then |
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