Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 1m
- 3. Describing Data Numerically2h 8m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables3h 28m
- 6. Normal Distribution & Continuous Random Variables2h 21m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 37m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals22m
- Confidence Intervals for Population Mean1h 26m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 20m
- 9. Hypothesis Testing for One Sample5h 13m
- Steps in Hypothesis Testing1h 13m
- Performing Hypothesis Tests: Means1h 1m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions39m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions29m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors15m
- 10. Hypothesis Testing for Two Samples5h 35m
- Two Proportions1h 12m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 2m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- Two Variances and F Distribution29m
- Two Variances - Graphing Calculator15m
- 11. Correlation1h 24m
- 12. Regression1h 59m
- 13. Chi-Square Tests & Goodness of Fit2h 31m
- 14. ANOVA2h 1m
10. Hypothesis Testing for Two Samples
Two Variances - Graphing Calculator
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Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
A historian is comparing the variation in weights of rare coins from different time periods. The data from two independent random samples is shown below. Using a 0.05 significance level and a graphing calculator, test the claim that the variation of weights before the 1900s is greater than after the 1900s.
: _________ : _________ -value: ______
Because -value [ < | > ] , we [ REJECT | FAIL TO REJECT ] , there is [ ENOUGH | NOT ENOUGH ] evidence to suggest…
A
Because P-value > α, we FAIL TO REJECT H0, there is ENOUGH evidence to suggest σ1=σ2.
B
Because P-value > α, we FAIL TO REJECT H0, there is NOT ENOUGH evidence to suggest σ1=σ2.
C
Because P-value < α, we REJECT H0, there is ENOUGH evidence to suggest σ1>σ2.
D
Because P-value < α, we REJECT H0, there is NOT ENOUGH evidence to suggest σ1>σ2.
Verified step by step guidance1
Step 1: Define the null hypothesis (\(H_0\)) and the alternative hypothesis (\(H_a\)) for the test of two variances. Typically, \(H_0: \sigma_1^2 = \sigma_2^2\) (the variances are equal) and \(H_a\) could be \(\sigma_1^2 \neq \sigma_2^2\), \(\sigma_1^2 > \sigma_2^2\), or \(\sigma_1^2 < \sigma_2^2\) depending on the context.
Step 2: Collect the sample variances (\(s_1^2\) and \(s_2^2\)) and sample sizes (\(n_1\) and \(n_2\)) from the two independent samples.
Step 3: Calculate the test statistic using the formula for the F-test for equality of variances:
\[F = \frac{s_1^2}{s_2^2}\]
where \(s_1^2\) is the larger sample variance to ensure \(F \geq 1\).
Step 4: Determine the degrees of freedom for the numerator and denominator:
\[df_1 = n_1 - 1\]
\[df_2 = n_2 - 1\]
Step 5: Use the TI-84 calculator's built-in F-distribution functions to find the p-value corresponding to the calculated \(F\) statistic and degrees of freedom, then compare the p-value to the significance level (\(\alpha\)) to decide whether to reject or fail to reject the null hypothesis.
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