In Problems 3–10, use the Wilcoxon matched-pairs signed-ranks test to test the given hypotheses at the \(\alpha\) = 0.05 level of significance. The dependent samples were obtained randomly.
Hypotheses: \(H_{0}: M_{D}\) = 0 versus \(H_{1}: M_{D}<0\) with n = 35 and \(T_{+}=210\).
Text Transcription:
\alpha
H_{0}: M_{D}
H_{1}: M_{D}<0
T_{+}=210
Step 1 of 5) The Wilcoxon matched-pairs signed-ranks test to test the given hypotheses at the a = 0.05 level of significance. The dependent samples were obtained randomly. 10. Hypotheses: H0: MD = 0 versus H1: MD 6 0 with n = 35 and T+ = 210. The interpretation of t is the same as that of the z-score. The t-statistic represents the number of sample standard errors x is from the population mean, m. It turns out that the shape of the t-distribution depends on the sample size, n. To help see how the t-distribution differs from the standard normal (or z-) distribution and the role that the sample size n plays, we will go through the following simulation. Comparing the Standard Normal Distribution to the t-Distribution Using Simulation (a) Use statistical software such as Minitab or StatCrunch to obtain 1500 simple random samples of size n = 5 from a normal population with m = 50 and s = 10. Calculate the sample mean and sample standard deviation for each sample. Compute z = x - mx s 1n and t = x - mx s 1n for each sample.