Using the t-distribution, it is found that there is not enough evidence that the diet is helping people lose weight.
At the null hypothesis, it is tested if the mean hasn't decreased, that is:
[tex]H_0: \mu_B \leq \mu_A[/tex]
[tex]H_0: \mu_B - \mu_A \leq 0[qtex]
At the alternative hypothesis, it is tested if the mean has decreased, that is:
[tex]H_1: \mu_B - \mu_A > 0[qtex]
For each sample, they are given as follows:
Hence, for the distribution of differences, they are:
The test statistic is:
[tex]t = \frac{\overline{x} - \mu}{s}[/tex]
In which [tex]\mu = 0[/tex] is the value tested at the null hypothesis.
Hence:
[tex]t = \frac{\overline{x} - \mu}{s}[/tex]
[tex]t = \frac{9.88 - 0}{11.66}[/tex]
t = 0.85.
Considering a right-tailed test with 9 + 9 - 2 = 16 df, with a standard significance level of 0.05, the critical value is t = 1.7459. Since t = 0.85 < 0.85, there is not enough evidence that the diet is helping people lose weight.
More can be learned about the t-distribution at https://brainly.com/question/13873630
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