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Ch. 10 - Chi-Square Tests and the F-Distribution
Larson - Elementary Statistics: Picturing the World 8th Edition
Larson8th EditionElementary Statistics: Picturing the WorldISBN: 9780137493470Not the one you use?Change textbook
Chapter 10, Problem 10.3.18

In Exercises 13–18, test the claim about the difference between two population variances σ₁² and σ₂² at the level of significance α. Assume the samples are random and independent, and the populations are normally distributed.


Claim: σ₁² > σ₂²; α = 0.05.
Sample statistics: s₁² = 44.6, n₁ = 16 and s₂² = 39.3, n₂ = 12

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Step 1: Identify the null and alternative hypotheses. The null hypothesis (H₀) is that the two population variances are equal (σ₁² = σ₂²), while the alternative hypothesis (H₁) is that the first population variance is greater than the second (σ₁² > σ₂²).
Step 2: Determine the test statistic for comparing two variances. Use the F-test statistic formula: F = s12s22, where s₁² and s₂² are the sample variances.
Step 3: Calculate the degrees of freedom for each sample. For the first sample, the degrees of freedom (df₁) is n₁ - 1, and for the second sample, the degrees of freedom (df₂) is n₂ - 1.
Step 4: Determine the critical value for the F-distribution at the given significance level (α = 0.05) and for a one-tailed test. Use the degrees of freedom (df₁ and df₂) to find the critical value from an F-distribution table or statistical software.
Step 5: Compare the calculated F-test statistic to the critical value. If the F-test statistic is greater than the critical value, reject the null hypothesis (H₀) and conclude that there is sufficient evidence to support the claim that σ₁² > σ₂². Otherwise, fail to reject the null hypothesis.

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Key Concepts

Here are the essential concepts you must grasp in order to answer the question correctly.

Hypothesis Testing

Hypothesis testing is a statistical method used to make decisions about population parameters based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1), then using sample statistics to determine whether to reject H0 in favor of H1. In this case, the null hypothesis would state that the variances are equal, while the alternative claims that one variance is greater than the other.
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Step 1: Write Hypotheses

F-Test for Variances

The F-test is a statistical test used to compare two population variances. It calculates the ratio of the two sample variances (s₁²/s₂²) and compares it to a critical value from the F-distribution based on the degrees of freedom of the samples. This test helps determine if there is a significant difference between the variances, which is essential for validating the claim that σ₁² is greater than σ₂².
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Level of Significance (α)

The level of significance, denoted as α, is the threshold for determining whether to reject the null hypothesis. It represents the probability of making a Type I error, which occurs when the null hypothesis is incorrectly rejected. In this scenario, α is set at 0.05, indicating a 5% risk of concluding that there is a difference in variances when there is none, guiding the decision-making process in hypothesis testing.
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Related Practice
Textbook Question

Conditional Relative Frequencies In Exercises 37–42, use the contingency table from Exercises 33–36, and the information below.

Relative frequencies can also be calculated based on the row totals (by dividing each row entry by the row’s total) or the column totals (by dividing each column entry by the column’s total). These frequencies are conditional relative frequencies and can be used to determine whether an association exists between two categories in a contingency table.


What percent of U.S. adults ages 25 and over who are employed have a degree?

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Textbook Question

List five properties of the F-distribution.

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Textbook Question

Explain how to determine the values of d.f.N and d.f.D when performing a two-sample F-test.

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Textbook Question

In Exercises 13–18, test the claim about the difference between two population variances σ₁² and σ₂² at the level of significance α. Assume the samples are random and independent, and the populations are normally distributed.


Claim: σ₁² ≤ σ₂²; α = 0.01.

Sample statistics: s₁² = 842, n₁ = 11 and s₂² = 836, n₂ = 10

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Textbook Question

Performing a Chi-Square Independence Test In Exercises 13–28, perform the indicated chi-square independence test by performing the steps below.

a. Identify the claim and state H₀ and Hₐ


b. Determine the degrees of freedom, find the critical value, and identify the rejection region.


c. Find the chi-square test statistic.


d. Decide whether to reject or fail to reject the null hypothesis.


e. Interpret the decision in the context of the original claim.


Achievement and School Location The contingency table shows the results of a random sample of students by the location of school and the number of those students achieving basic skill levels in three subjects. At α=0.01, test the hypothesis that the variables are independent. (Adapted from HUD State of the Cities Report)


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Textbook Question

Performing a Two-Sample F-Test In Exercises 19–26, (a) identify the claim and state H0 and Ha, (b) find the critical value and identify the rejection region, (c) find the test statistic F, (d) decide whether to reject or fail to reject the null hypothesis, and (e) interpret the decision in the context of the original claim. Assume the samples are random and independent, and the populations are normally distributed.


U.S. History Assessment Tests A state school administrator claims that the standard deviations of U.S. history assessment test scores for eighth-grade students are the same in Districts 1 and 2. A sample of 10 test scores from District 1 has a standard deviation of 30.9 points, and a sample of 13 test scores from District 2 has a standard deviation of 27.2 points. At α=0.01, can you reject the administrator’s claim? (Adapted from National Center for Education Statistics)

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