Learn by DoingIn this activity we practice the Chi-square Test for Independence in its entirety using StatCrunch.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 Learn by Doing discussion board exercises. A list of StatCrunch directions is provided at the bottom of this page.Context – A Real Court CaseIn the early 1970s, a young man challenged an Oklahoma state law that prohibited the sale of 3.2% beer to males under age 21 but allowed its sale to females in the same age group. The case (Craig v. Boren, 429 U.S. 190, 1976) was ultimately heard by the U.S. Supreme Court. The state of Oklahoma argued that the law improved traffic safety. One of the three main pieces of data presented to the court was the result of a “random roadside survey.” This survey gathered information on gender and whether or not the driver had been drinking alcohol in the previous 2 hours. A total of 619 drivers under 21 years of age were included in the survey.PromptA test of independence may be appropriate if we are examining the relationship between two categorical variables in one population. For this situation what is the population? What is the explanatory variable? What is the response variable?What are the hypotheses for the Test of Independence? State hypotheses with reference to the context of the scenario.The spreadsheet of the data looked like this:

Roadside survey dataDriverGenderAlcohol in lasttwo hours?Driver 1MYesDriver 2FNoDriver 3FYes………Driver 619MNo We will not use the raw data. Instead we will use the summarized data shown in the table below.

Roadside survey summaryDrank alcohol in last 2 hours?YesNoTotals Male77404481 Female16122138 Totals93526619Use StatCrunch to find expected counts, the Chi-square test statistic and the P-value. (directions) Copy and paste your StatCrunch table into your post. How many males in the sample are expected to answer yes to question about alcohol consumption in the last two hours? Show how to calculate this expected count and explain what it means relative to the hypotheses.Explain how we know that this data meets the conditions for use of a chi-square distribution.State a conclusion at a 5% level of significance. Do you think that the data supports the Oklahoma law that forbids sale of 3.2% beer to males and permits it to females?

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