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(Solved): Fill in the missing entries in the analysis of variance table for a simple linear regression analy ...



Fill in the missing entries in the analysis of variance table for a simple linear regression analysis, (Round your mean squar

Fill in the missing entries in the analysis of variance table for a simple linear regression analysis, (Round your mean squares to three decimal places and your \( F \) statistic to two decimal places.) Test for a significant regression with \( \alpha=0.05 \). State the null and alternative hypotheses. \[ \begin{array}{l} H_{0}: \beta \neq 0 \text { versus } H_{a}: \beta=0 \\ H_{0}: \beta=0 \text { versus } H_{a}: \beta>0 \\ H_{0}: \beta=0 \text { versus } H_{a}: \beta<0 \\ H_{0}: \beta<0 \text { versus } H_{a}: \beta>0 \\ H_{0}: \beta=0 \text { versus } H_{a}: \beta \neq 0 \end{array} \] Find the test statistic. (Round your answer to two decimal places.) \[ F= \] Find the rejection region. (Round your answers to two decimal places.) \[ F> \] State your conclusion. \( \mathrm{H}_{0} \) is rejected. There is insufficient evidence to suggest a significant linear regression. \( \mathrm{H}_{0} \) is rejected. There is sufficient evidence to suggest a significant linear regression. \( \mathrm{H}_{0} \) is not rejected. There is sufficient evidence to suggest a significant linear regression. \( \mathrm{H}_{0} \) is not rejected. There is insufficient evidence to suggest a significant linear regression. Calculate the coefficient of determination, \( r^{2} \). (Round your answer to three decimal places.) \[ r^{2}= \] Interpret the significance of \( r^{2} \). The value of \( r^{2} \) is very large which suggests that \( \beta \) is very large. The value of \( r^{2} \) is relatively small which suggests that a relatively small proportion of the variation in the response can be explained by the predictor variable. The value of \( r^{2} \) is very large which suggests that \( \beta \) is very small. The value of \( r^{2} \) is very large which suggests that a large proportion of the variation in the response can be explained by the predictor variable. The value of \( r^{2} \) is relatively small which suggests that \( \beta \) is very large. You may need to use the appropriate appendix table or technology to answer this question.


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Fill in the missing entries in the analysis of variance table for a simple linear regression analysis (Round
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