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American Idol Contestants on the TV show American Idol competed to win a singing contest. At one point, the website WhatNotToSing.com listed the actual numbers of eliminations for different orders of singing, and the expected number of eliminations was also listed. The results are in the table below. Use a 0.05 significance level to test the claim that the actual eliminations agree with the expected numbers. Does there appear to be support for the claim that the leadoff singers appear to be at a disadvantage?

Singing Order

1

2

3

4

5

6

7–12

Actual Eliminations

20

12

9

8

6

5

9

Expected Eliminations

12.9

12.9

9.9

7.9

6.4

5.5

13.5

Short Answer

Expert verified

There is not enough evidence to conclude thatthe actual number of eliminations is not the same as the expected number of eliminations.

Since the actual number of eliminations for the lead-off singers is 20 and the expected eliminations should have been 12.9, it can be said that the lead-off singers appear to be at a disadvantage.

Step by step solution

01

Given information

The actual number and the expected number of eliminations are tabulated for different orders for singing.

02

Hypotheses

The null hypothesis is as follows:

The actual number of eliminations is the same as the expected number of eliminations.

The alternative hypothesis is as follows:

The actual number of eliminations is not the same as the expected number of eliminations.

It is considered a right-tailed test.

If the test statistic value is greater than the critical value, then the null hypothesis is rejected, otherwise not.

03

Observed and expected frequencies

Observed Frequency:

The frequencies provided in the table are the observed frequencies. They are denoted by\(O\).

The following table shows the observed frequency for each order:

\({O_1}\)=20

\({O_2}\)=12

\({O_3}\)=9

\({O_4}\)=8

\({O_5}\)=6

\({O_6}\)=5

\({O_{7 - 12}}\)=9

Expected Frequency:

The expected frequencies are also tabulated in the problem. The values are written below:

\({E_1}\)=12.9

\({E_2}\)=12.9

\({E_3}\)=9.9

\({E_4}\)=7.9

\({E_5}\)=6.4

\({E_6}\)=5.5

\({E_{7 - 12}}\)=13.5

04

Test statistic

The test statistic is computed as shown below:

\[\begin{aligned}{c}{\chi ^2} = \sum {\frac{{{{\left( {O - E} \right)}^2}}}{E}\;\;\; \sim } {\chi ^2}_{\left( {k - 1} \right)}\\ = \frac{{{{\left( {20 - 12.9} \right)}^2}}}{{12.9}} + \frac{{{{\left( {12 - 12.9} \right)}^2}}}{{12.9}} + ....... + \frac{{{{\left( {9 - 13.5} \right)}^2}}}{{13.5}}\\ = 5.624\end{aligned}\]

Thus, \({\chi ^2} = 5.624\).

05

Obtain critical value, p-value and determine the conclusion of the test

The degrees of freedom are computed below:

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

The critical value of\({\chi ^2}\)for 6 degrees of freedom at 0.05 level of significance for a right-tailed test is equal to 12.5916.

The corresponding p-value is approximately equal to 0.466598.

Since the value of the test statistic is less than the critical value and the p-value is greater than 0.05, the null hypothesis is failed to reject.

There is not enough evidence to conclude thatthe actual number of eliminations is not the same as the expected number of eliminations.

Since the actual number of eliminations for the lead-off singers is 20 and the expected eliminations should have been 12.9, it can be said that the lead off singers appear to be at a disadvantage.

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Most popular questions from this chapter

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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American-born baseball players have birth dates in the months immediately following July 31 because that was the age cutoff date for nonschool baseball leagues. The table below lists months of births for a sample of American-born baseball players and foreign-born baseball players. Using a 0.05 significance level, is there sufficient evidence to warrant rejection of the claim that months of births of baseball players are independent of whether they are born in America? Do the data appear to support Gladwell’s claim?


Born in America

Foreign Born

Jan.

387

101

Feb.

329

82

March

366

85

April

344

82

May

336

94

June

313

83

July

313

59

Aug.

503

91

Sept.

421

70

Oct.

434

100

Nov.

398

103

Dec.

371

82

Benford’s Law. According to Benford’s law, a variety of different data sets include numbers with leading (first) digits that follow the distribution shown in the table below. In Exercises 21–24, test for goodness-of-fit with the distribution described by Benford’s 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’s 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’s law. Does the conclusion change if the significance level is 0.05?

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