Learn by DoingIn this activity you will learn to use StatCrunch to perform a two-sample t-test.Some features of this activity may not work well on a cell phone or tablet. We highly recommend that you complete this activity on a computer.Here are the directions and grading rubric for the discussion board exercises. A list of StatCrunch directions is provided at the bottom of this page.ContextDo undergraduates sleep less than graduate students?A student conducted a study of sleep habits at a large state university. His hypothesis is that undergraduates will party more and sleep less than graduate students. He surveyed random samples of 75 undergraduate students and 50 graduate students. Subjects reported the hours they sleep in a typical night.For this hypothesis test, he defined the population means as follows: μ 1 is the mean number of hours undergraduate students sleep in a typical night. μ 2 is the mean number of hours graduate students sleep in a typical night.VariablesHours: typical number of hours a student sleeps each night Program: undergraduate or graduate Program is the explanatory variable, and the data is categorical. Hours is the response variable, and the data is quantitative.DataTwo sample T hypothesis test:μ1 : Mean of undergraduateμ2 : Mean of graduateμ1 - μ2 : Difference between two meansH0 : μ1 - μ2 = 0HA : μ1 - μ2 < 0(without pooled variances)Hypothesis test results: DifferenceSample Diff.Std. Err.DFT-StatP-value μ1 - μ2-0.233333330.18963708106.32776-1.23042040.1106 PromptState the null and alternative hypotheses. Include a clear description of the populations and the variable.Explain why we can safely use the two-sample T-test in this case.Use StatCrunch to carry out the test. Copy and paste the content of in the StatCrunch output window (text and the table) in your initial post. State a conclusion in the context of this problem.ANSWER(S): (hint)Since we want to check whether the data supports the claim that undergraduate students sleep less, on average, than graduate students, we are testing: H 0 : μ 1 − μ 2 = 0 H a : μ 1 − μ 2 < 0 We can safely use the two-sample T-test in this case since: Both samples are random, and therefore independent.The sample sizes (75 and 50) are quite large, and therefore we can proceed regardless of whether the populations are normal or not. T = -1.23; P-value = 0.111Here is the StatCrunch output depicting these values.Two sample T hypothesis test: μ 1 : Mean of undergraduate μ 2 : Mean of graduate H 0 : μ 1 − μ 2 = 0 H a : μ 1 − μ 2 < 0 (without pooled variances) Hypothesis test results: Difference Sample Diff. Sd. Err. DF T-Stat P-Value μ1 - μ2 -0.23333333 0.18963708 106.32776 -1.2304204 0.1106 The P-value (0.111) is greater than 0.05. This indicates that the observed data is not unusual if undergraduate and graduate students, on average, have the same mean for sleep hours. Therefore, the data do not provide evidence to reject H 0 . So we cannot conclude that undergraduate students sleep less, on average, than graduate students.

Small boxes of NutraFlakes cereal are labeled “net weight 10 ounces.” Each hour, random samples of size n = 4 boxes are weighed to check process control. Five hours of observations yielded the following: Weight Time Box 1 Box 2 Box 3 Box 4 9 A.M. 9.8 10.4 9.9 10.3 10 A.M. 10.1 10.2 9.9 9.8 11 A.M. 9.9 10.5 10.3 10.1 Noon 9.7 9.8 10.3 10.2 1 P.M. 9.7 10.1 9.9 9.9 Using these data, construct limits for x- and R- charts. Is the process in control? What other steps should the QC department follow at this point?