Motor Vehicle Fatalities The table below lists motor vehicle fatalities by day of the week for a recent year (based on data from the Insurance Institute for Highway Safety). Use a 0.01 significance level to test the claim that auto fatalities occur on the different days of the week with the same frequency. Provide an explanation for the results.

Day

Sun.

Mon.

Tues.

Wed.

Thurs.

Fri.

Sat.

Frequency

5304

4002

4082

4010

4268

5068

5985

Short Answer

Expert verified

There is enough evidence to conclude thatmotor vehicle fatalities do not occur equally frequently on all days of the week.

The number of auto fatalities is higher on weekends than on weekdays because drinking activity increases on weekends (since individuals have more free time) and as a result, people tend to drive under the influence of alcohol which leads to accidents.

Step by step solution

01

Given information

Data are given on the frequency of auto fatalities on different days of the week.

The claim is to test that auto fatalities occur on different days of the week with the same frequency at the significance level 0.01.

02

Hypotheses

The null hypothesis is as follows:

Motor vehicle fatalities occur with equal frequency on all days of the week.

The alternative hypothesis is as follows:

Motor vehicle fatalities do not occur with equal frequency on all days of the week.

03

Observed and expected frequencies

Let 1, 2, …..,6 represent the different days of the week starting from Sunday and ending on Saturday.

The observed frequencies of fatalities are tabulated as shown:

\({O_1}\)=5304

\({O_2}\)=4002

\({O_3}\)=4082

\({O_4}\)=4010

\({O_5}\)=4268

\({O_6}\)=5068

\({O_7}\)=5985

It is given that fatalities are believed to occur with the same frequency on different days.

Therefore, the expected frequency for each of the 7 days is the same and is equal to:

\(E = \frac{n}{k}\)

Where,

n is the total frequency

k is the number of days.

The value of n is computed as shown:

\(\begin{aligned}{c}n = 5304 + 4002 + .... + 5985\\ = 32719\end{aligned}\)

The value of k is equal to 7.

Thus, the expected frequency is equal to:

\(\begin{aligned}{c}E = \frac{n}{k}\\ = \frac{{32719}}{7}\\ = 4674.143\end{aligned}\)

04

Test statistic

The test statistic value is computed as shown:

\[\begin{aligned}{c}{\chi ^2} = \sum {\frac{{{{\left( {O - E} \right)}^2}}}{E}} \;\; \sim {\chi ^2}_{\left( {k - 1} \right)}\\ = \frac{{{{\left( {5304 - 4674.143} \right)}^2}}}{{4674.143}} + \frac{{{{\left( {4002 - 4674.143} \right)}^2}}}{{4674.143}} + ....... + \frac{{{{\left( {5985 - 4674.143} \right)}^2}}}{{4674.143}}\\ = 787.018\end{aligned}\]

Thus, \({\chi ^2} = 787.018\).

05

Critical value, p-value and conclusion of the test

The degrees of freedom are computed as shown:

\(\begin{aligned}{c}df = k - 1\\ = 7 - 1\\ = 6\end{aligned}\)

The critical value of\({\chi ^2}\)with 6 degrees of freedom and\(\alpha = 0.01\)for a right-tailed test is equal to 16.8119.

Referring to the chi-square table, the p-value is obtained using the degrees of freedom and test statistic, which is equal to 0.000.

Since the value of the test statistic is greater than the critical value and the p-value is less than 0.01, the null hypothesis is rejected.

Thus, there is enough evidence to conclude thatmotor vehicle fatalities do not occur equally frequently on all days of the week.

06

Explanation for the Conclusion

The number of auto fatalities is higher on weekends as compared to weekdays because drinking activity increases on weekends (as people have more free time) and thus, people tend to drive under the influence of alcohol which leads to accidents.

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Most popular questions from this chapter

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 “Four Quarters the Same as $1?” 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.

Car Repair Costs Listed below are repair costs (in dollars) for cars crashed at 6 mi/h in full-front crash tests and the same cars crashed at 6 mi/h in full-rear crash tests (based on data from the Insurance Institute for Highway Safety). The cars are the Toyota Camry, Mazda 6, Volvo S40, Saturn Aura, Subaru Legacy, Hyundai Sonata, and Honda Accord. Is there sufficient evidence to conclude that there is a linear correlation between the repair costs from full-front crashes and full-rear crashes?

Front

936

978

2252

1032

3911

4312

3469

Rear

1480

1202

802

3191

1122

739

2767

Exercises 1–5 refer to the sample data in the following table, which summarizes the last digits of the heights (cm) of 300 randomly selected subjects (from Data Set 1 “Body Data” in Appendix B). Assume that we want to use a 0.05 significance level to test the claim that the data are from a population having the property that the last digits are all equally likely.

Last Digit

0

1

2

3

4

5

6

7

8

9

Frequency

30

35

24

25

35

36

37

27

27

24

Given that the P-value for the hypothesis test is 0.501, what do you conclude? Does it appear that the heights were obtained through measurement or that the subjects reported their heights?

In Exercises 5–20, conduct the hypothesis test and provide the test statistic and the P-value and , or critical value, and state the conclusion.

California Daily 4 Lottery The author recorded all digits selected in California’s Daily 4 Lottery for the 60 days preceding the time that this exercise was created. The frequencies of the digits from 0 through 9 are 21, 30, 31, 33, 19, 23, 21, 16, 24, and 22. Use a 0.05 significance level to test the claim of lottery officials that the digits are selected in a way that they are equally likely.

Questions 6–10 refer to the sample data in the following table, which describes the fate of the passengers and crew aboard the Titanic when it sank on April 15, 1912. Assume that the data are a sample from a large population and we want to use a 0.05 significance level to test the claim that surviving is independent of whether the person is a man, woman, boy, or girl.


Men

Women

Boys

Girls

Survived

332

318

29

27

Died

1360

104

35

18

Is the hypothesis test left-tailed, right-tailed, or two-tailed?

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