3. Using Table \( G \), find the values for \( \chi_{\text {left }}^{2} \) and \( \chi_{\text {right }}^{2} \) a. \( \alpha=0.05, n=12 \) b. \( \alpha=0.10, n=20 \) c. \( \alpha=0.05, n=27 \) d. \( \alpha=0.01, n=6 \) e. \( \alpha=0.10, n=41 \)
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Bonus Knowledge
To find the values for \( \chi_{\text {left }}^{2} \) and \( \chi_{\text {right }}^{2} \) using a chi-square distribution table, you typically need to determine the degrees of freedom, which is based on the sample size. The degrees of freedom for chi-square tests is calculated as \( df = n - 1 \). Now, let's delve into the scenarios you provided: - For \( \alpha = 0.05, n = 12 \): The degrees of freedom is \( 12 - 1 = 11 \). Using a chi-square table, \( \chi_{\text{left}}^{2} \) is typically the value for \( \alpha/2 \) and \( \chi_{\text{right}}^{2} \) for \( 1 - \alpha/2 \). - For \( \alpha = 0.10, n = 20 \): Here, \( df = 20 - 1 = 19 \). Look up the critical values corresponding to \( 0.05 \) and \( 0.95 \) in the table to find your chi-square values. For the remaining scenarios, you would apply the same method: - \( \alpha = 0.05, n = 27 \) leads to \( df = 26 \). - \( \alpha = 0.01, n = 6 \) leads to \( df = 5 \). - \( \alpha = 0.10, n = 41 \) leads to \( df = 40 \). Once you identify the degree of freedom, you can retrieve the critical values from the chi-square table based on your chosen alpha levels. Each configuration will yield a different pair of critical values for \( \chi_{\text{left}}^{2} \) and \( \chi_{\text{right}}^{2} \). Enthusiastic learners often fail to check for continuity corrections when approximating chi-squared distributions, especially with small sample sizes. Always double-check that you're using the right critical values tailored to your specific degrees of freedom!