Question

***** Please answer all questions, not some. This entire
discussion does count as one question w/
parts.**

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**Context - A Real Court Case**

In 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.

**Prompt**

- A 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 data **Driver****Gender****Alcohol in last**

two hours?Driver 1 M Yes Driver 2 F No Driver 3 F Yes .

.

..

.

..

.

.Driver 619 M No

We will not use the raw data. Instead we will use the summarized data shown in the table below.

Roadside survey summary **Drank alcohol in last 2 hours?****Yes****No**Totals **Male**77 404 481 **Female**16 122 138 Totals 93 526 619

Use StatCrunch to find expected counts, the Chi-square test statistic and the P-value. (directions)

Copy and paste your StatCrunch table into your post.

*This is #3 completed:*Contingency table results:

Rows: Gender

Columns: NoneCell format Count

(Expected count)Alcohol Yes Alcohol No Total Male 77

(73.45)404

(407.55)481 Female 16

(19.55)112

(108.45)128 Total 93 516 609 Chi-Square test:

Statistic DF Value P-value Chi-square 1 0.96169443 0.3268 - 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?

Answer #1

1.

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