We want to conduct a hypothesis test of the claim that the population mean reading speed of second graders is different from 30.2 words per minute. So, we choose a random sample of students' reading speeds. The sample has a mean of 30.7 words per minute and a standard deviation of 3 words per minute, For each of the following sampling scenarios, choose an appropriate test statistic for our hypothesis test on the population mean. Then calculate that statistic. Round your answers to two decimal places. (a) The sample has size 100, and it is from a non-normally distributed population with a known standard deviation of 3. \[ =\square \] It is unclear which test statistic to use. (b) The sample has size 11, and it is from a normally distributed population with an unknown standard deviation. \( z=\square \) \( i=\square \)
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When dealing with a sample size of 100 from a non-normally distributed population with a known standard deviation, you would typically use a z-test statistic for a hypothesis test. The formula for the z-test statistic is given by: \[ z = \frac{\bar{x} - \mu}{\sigma / \sqrt{n}} \] Here: - \(\bar{x} = 30.7\) (sample mean) - \(\mu = 30.2\) (null hypothesis mean) - \(\sigma = 3\) (known population standard deviation) - \(n = 100\) (sample size) Plugging in these values: \[ z = \frac{30.7 - 30.2}{3 / \sqrt{100}} = \frac{0.5}{0.3} = 1.67 \] So, for (a): \[ z = 1.67 \] For part (b), with a sample size of 11 from a normally distributed population and an unknown standard deviation, you would use a t-test statistic. The formula for the t-test statistic is: \[ t = \frac{\bar{x} - \mu}{s / \sqrt{n}} \] In this case: - \(\bar{x} = 30.7\) (sample mean) - \(\mu = 30.2\) (null hypothesis mean) - \(s = 3\) (sample standard deviation, in this case assumed known) - \(n = 11\) (sample size) Plugging in these values: \[ t = \frac{30.7 - 30.2}{3 / \sqrt{11}} \approx \frac{0.5}{0.90} \approx 0.56 \] So, for (b): \( t = 0.56 \) In summary: (a) \( z = 1.67 \) (b) \( t = 0.56 \)