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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.

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