Describe the difference between the variance between samples MSB and the variance within samples MSW.
Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 53m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample2h 19m
- 10. Hypothesis Testing for Two Samples3h 22m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
14. ANOVA
Introduction to ANOVA
Problem 10.3.15
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

1
Step 1: Identify the null hypothesis (H₀) and the alternative hypothesis (H₁). The claim is that σ₁² ≤ σ₂². Therefore, the null hypothesis is H₀: σ₁² ≤ σ₂², and the alternative hypothesis is H₁: σ₁² > σ₂². This is a one-tailed test.
Step 2: Calculate the test statistic using the F-distribution formula: F = (s₁² / s₂²), where s₁² and s₂² are the sample variances. Substitute the given values: s₁² = 842 and s₂² = 836.
Step 3: Determine the degrees of freedom for the numerator (df₁) and denominator (df₂). For the numerator, df₁ = n₁ - 1, and for the denominator, df₂ = n₂ - 1. Substitute the sample sizes n₁ = 11 and n₂ = 10 to calculate df₁ and df₂.
Step 4: Find the critical value of F from the F-distribution table at the significance level α = 0.01 for a one-tailed test, using the calculated degrees of freedom (df₁ and df₂).
Step 5: Compare the calculated F value to the critical F value. If the calculated F value is greater than the critical F value, reject the null hypothesis H₀. Otherwise, fail to reject H₀. Interpret the result in the context of the claim.

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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 this case, the null hypothesis states that the variance of the first population is less than or equal to that of the second (σ₁² ≤ σ₂²).
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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 in the question.
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Variance & Standard Deviation of Discrete Random Variables
Level of Significance (α)
The level of significance, denoted as α, is the probability of rejecting the null hypothesis when it is actually true (Type I error). In this scenario, α is set at 0.01, indicating a 1% risk of concluding that a difference exists when there is none. This threshold helps determine the critical value for the F-test and influences the strength of evidence required to support the claim.
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