The following data are available for a simple linear regression analysis attempting to link hours of training (x) to hourly output (y). In applying the least squares criterion, the slope (b) and the intercept (a) for the best-fitting line are b = 2.4 and a = 3.6.Set up a hypothesis test to determine whether you can reject the hypothesis that the population slope, β, is 0 at the 5% significance level.Compute the value of the appropriate sample test statistic, t_sta_t, and use it to reach the proper conclusion. A)Since t_stat is outside the critical values of ±3.213, we can reject the β=0 null hypothesis. B)Since t_stat is between the critical values of ±3.213, we can't reject the β=0 null hypothesis. C)Since t_stat is outside the critical values of ±4.403, we can reject the β=0 null hypothesis. D)Since t_stat is between the critical values of ±4.403, we can't reject the β=0 null hypothesis.

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The Answer of The following data are available for a simple linear regression analysis attempting to link hours of training (x) to hourly output (y). In applying the least squares criterion, the slope (b) and the intercept (a) for the best-fitting line are b = 2.4 and a = 3.6.Set up a hypothesis test to determine whether you can reject the hypothesis that the population slope, β, is 0 at the 5% significance level.Compute the value of the appropriate sample test statistic, t_sta_t, and use it to reach the proper conclusion. A)Since t_stat is outside the critical values of ±3.213, we can reject the β=0 null hypothesis. B)Since t_stat is between the critical values of ±3.213, we can't reject the β=0 null hypothesis. C)Since t_stat is outside the critical values of ±4.403, we can reject the β=0 null hypothesis. D)Since t_stat is between the critical values of ±4.403, we can't reject the β=0 null hypothesis.

The Following Data Are Available for a Simple Linear Regression