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Two Variances - Graphing Calculator quiz

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  • What is the purpose of a two-sample F test for variance?
    It compares sample standard deviations to test hypotheses about population variances.
  • What is the typical null hypothesis in a two-sample F test for variance?
    The null hypothesis usually states that σ1 equals σ2.
  • What does the alternative hypothesis represent in a two-sample F test?
    It reflects the claim being tested, such as σ1 > σ2.
  • What significance level (alpha) was used in the example problems?
    Alpha was set at 0.05 in both example scenarios.
  • What are the two main ways to input data into the graphing calculator for the F test?
    You can input either sample statistics or raw data.
  • Which calculator menu do you use to access the two-sample F test?
    You use the stat menu and then go to the tests tab.
  • What four inputs are required when entering sample statistics for the F test?
    You need s1, s2, n1, and n2.
  • How do you decide which sample is s1 when entering statistics?
    s1 should be the sample with the larger standard deviation.
  • What two values does the calculator provide after running the F test?
    It provides the F statistic and the p-value.
  • What does a p-value less than alpha indicate in the F test?
    It means you reject the null hypothesis, indicating significant variance differences.
  • What is the conclusion if the p-value is greater than alpha?
    You fail to reject the null hypothesis, meaning there is not enough evidence for the alternative.
  • How do you input raw data for the F test on a graphing calculator?
    Enter the data into lists L1 and L2, then select the data tab in the F test menu.
  • What should you check when entering data for the F test?
    Ensure sample one and sample two are correctly labeled as L1 and L2.
  • What type of test should you select if the claim is σ1 > σ2?
    You should select a right-tailed test.
  • What is the main advantage of using the two-sample F test function on a graphing calculator?
    It streamlines hypothesis testing for variance, making the process faster and more accurate.