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Equivalent Tests A\({\chi ^2}\)test involving a 2\( \times \)2 table is equivalent to the test for the differencebetween two proportions, as described in Section 9-1. Using the claim and table inExercise 9 鈥淔our Quarters the Same as $1?鈥 verify that the\({\chi ^2}\)test statistic and the zteststatistic (found from the test of equality of two proportions) are related as follows:\({z^2}\)=\({\chi ^2}\).

Also show that the critical values have that same relationship.

Short Answer

Expert verified

The critical values and the test statistic of \({\chi ^2}\;{\rm{and}}\;{z^2}\) shows the same relationship; that is \({\chi ^2} = {z^2}\).

Step by step solution

01

Given information

The data for the students, whether they purchased gum or kept the money,is provided.

02

Compute the test statistic

Referring to Exercise 9 of section 11-2,

The value of the chi-square test statistic is 12.162.

From Table A-4, the critical value for the row correspondsto 1 degree of freedom and at 0.05 level of significance 3.841.

Therefore, the critical value is 3.841.

03

Compute the proportions and z test statistic

Let\({\hat p_1}\)representthe sample proportion of students who purchased the gum and students given four quarters.

Let\({\hat p_2}\)representthe sample proportion of students who purchased the gum and students given a $1 Bill.

The proportions are computed as,

\(\begin{aligned}{c}{{\hat p}_1} = \frac{{27}}{{27 + 16}}\\ = 0.628\end{aligned}\)

Similarly,

\(\begin{aligned}{c}{{\hat p}_2} = \frac{{12}}{{12 + 34}}\\ = 0.261\end{aligned}\)

The value of the pooled sample proportion is computed as follows:

\(\begin{aligned}{c}\bar p = \frac{{{x_1} + {x_2}}}{{{n_1} + {n_2}}}\\ = \frac{{12 + 27}}{{46 + 43}}\\ = 0.438\end{aligned}\)

\(\begin{aligned}{c}\bar q = 1 - \bar p\\ = 1 - 0.438\\ = 0.562\end{aligned}\)

The value of the test statistic is computed below:

\(\begin{aligned}{c}z = \frac{{\left( {{{\hat p}_1} - {{\hat p}_2}} \right) - \left( {{p_1} - {p_2}} \right)}}{{\sqrt {\frac{{\bar p\bar q}}{{{n_1}}} + \frac{{\bar p\bar q}}{{{n_2}}}} }}\;\;\;\;{\rm{where}}\left( {{p_1} - {p_2}} \right) = 0\\ = \frac{{\left( {0.261 - 0.628} \right) - 0}}{{\sqrt {\frac{{\left( {0.438} \right)\left( {0.562} \right)}}{{46}} + \frac{{\left( {0.438} \right)\left( {0.562} \right)}}{{43}}} }}\\ = - 3.487395274\end{aligned}\)

Thus, the value of z test statistic is -3.487395274.

The critical value of z corresponding to \(\alpha = 0.05\) for a two-tailed test is equal to \( \pm \)1.96.

04

Show the relationship

The calculations are as follows,

For test statistic:

\(\begin{aligned}{c}{z^2} = {\left( { - 3.487395274} \right)^2}\\ = 12.162\end{aligned}\)

Thus,\({\chi ^2} = {z^2}\)

For critical values:

\(\begin{aligned}{c}{z^2} = {\left( {1.96} \right)^2}\\ = 3.841\end{aligned}\)

Thus,\({\chi ^2} = {z^2}\)

Therefore, the critical value of chi-square and square of z critical value is approximately the same.

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Most popular questions from this chapter

Loaded Die The author drilled a hole in a die and filled it with a lead weight, then proceeded to roll it 200 times. Here are the observed frequencies for the outcomes of 1, 2, 3, 4, 5, and 6, respectively: 27, 31, 42, 40, 28, and 32. Use a 0.05 significance level to test the claim that the outcomes are not equally likely. Does it appear that the loaded die behaves differently than a fair die?

Winning team data were collected for teams in different sports, with the results given in the table on the top of the next page (based on data from 鈥淧redicting Professional Sports Game Outcomes fromIntermediateGame Scores,鈥 by Copper, DeNeve, and Mosteller, Chance,Vol. 5, No. 3鈥4). Use a 0.10significance level to test the claim that home/visitor wins are independent of the sport. Given that among the four sports included here, baseball is the only sport in which the home team canmodify field dimensions to favor its own players, does it appear that baseball teams are effective in using this advantage?

Basketball

Baseball

Hockey

Football

Home Team Wins

127

53

50

57

Visiting Team Wins

71

47

43

42

A case-control (or retrospective) study was conductedto investigate a relationship between the colors of helmets worn by motorcycle drivers andwhether they are injured or killed in a crash. Results are given in the table below (based on datafrom 鈥淢otorcycle Rider Conspicuity and Crash Related Injury: Case-Control Study,鈥 by Wellset al., BMJ USA,Vol. 4). Test the claim that injuries are independent of helmet color. Shouldmotorcycle drivers choose helmets with a particular color? If so, which color appears best?

Color of helmet


Black

White

Yellow/Orange

Red

Blue

Controls (not injured)

491

377

31

170

55

Cases (injured or killed)

213

112

8

70

26

Benford鈥檚 Law. According to Benford鈥檚 law, a variety of different data sets include numbers with leading (first) digits that follow the distribution shown in the table below. In Exercises 21鈥24, test for goodness-of-fit with the distribution described by Benford鈥檚 law.

Leading Digits

Benford's Law: Distributuon of leading digits

1

30.10%

2

17.60%

3

12.50%

4

9.70%

5

7.90%

6

6.70%

7

5.80%

8

5.10%

9

4.60%

Author鈥檚 Check Amounts Exercise 21 lists the observed frequencies of leading digits from amounts on checks from seven suspect companies. Here are the observed frequencies of the leading digits from the amounts on the most recent checks written by the author at the time this exercise was created: 83, 58, 27, 21, 21, 21, 6, 4, 9. (Those observed frequencies correspond to the leading digits of 1, 2, 3, 4, 5, 6, 7, 8, and 9, respectively.) Using a 0.01 significance level, test the claim that these leading digits are from a population of leading digits that conform to Benford鈥檚 law. Does the conclusion change if the significance level is 0.05?

The accompanying table is from a study conducted

with the stated objective of addressing cell phone safety by understanding why we use a particular ear for cell phone use. (See 鈥淗emispheric Dominance and Cell Phone Use,鈥 by Seidman, Siegel, Shah, and Bowyer, JAMA Otolaryngology鈥擧ead & Neck Surgery,Vol. 139, No. 5.)

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a. Use the data in the table to find the expected value for the cell that has an observed frequency of 3. Round the result to three decimal places.

b. What does the expected value indicate about the requirements for the hypothesis test?

Right Ear

Left Ear

No Preference

Right-Handed

436

166

40

Left-Handed

16

50

3

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