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Lecture Slides

Lecture Slides. Elementary Statistics Twelfth Edition and the Triola Statistics Series by Mario F. Triola. Chapter 8 Hypothesis Testing. 8-1 Review and Preview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim about a Proportion 8-4 Testing a Claim About a Mean

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Lecture Slides

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  1. Lecture Slides Elementary StatisticsTwelfth Edition and the Triola Statistics Series by Mario F. Triola

  2. Chapter 8Hypothesis Testing 8-1 Review and Preview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim about a Proportion 8-4 Testing a Claim About a Mean 8-5 Testing a Claim About a Standard Deviation or Variance

  3. Key Concept This section introduces methods for testing a claim made about a population standard deviation σ or population variance σ2. The methods of this section use the chi-square distribution that was first introduced in Section 7-4.

  4. Requirements for Testing Claims About σor σ2 = sample size = sample standard deviation = sample variance  = claimed value of the population standard deviation = claimed value of the population variance

  5. Requirements 1. The sample is a simple random sample. 2. The population has a normal distribution. (This is a much stricter requirement than the requirement of a normal distribution when testing claims about means.)

  6. Chi-Square Distribution Test Statistic

  7. P-Values and Critical Values for Chi-Square Distribution P-values: Use technology or Table A-4. Critical Values: Use Table A-4. In either case, the degrees of freedom = n –1.

  8. Caution The χ2test of this section is not robust against a departure from normality, meaning that the test does not work well if the population has a distribution that is far from normal. The condition of a normally distributed population is therefore a much stricter requirement in this section than it was in Section 8-4.

  9. Properties of Chi-Square Distribution All values of χ2 are nonnegative, and the distribution is not symmetric (see the Figure on the next slide). There is a different distribution for each number of degrees of freedom. The critical values are found in Table A-4 using n – 1 degrees of freedom.

  10. Properties of Chi-Square Distribution Chi-Square Distribution for 10 and 20 df Properties of the Chi-Square Distribution Different distribution for each number of df.

  11. Example Listed below are the heights (inches) for a simple random sample of ten supermodels. Consider the claim that supermodels have heights that have much less variation than the heights of women in the general population. We will use a 0.01 significance level to test the claim that supermodels have heights with a standard deviation that is less than 2.6 inches. Summary Statistics:

  12. Example - Continued Requirement Check: The sample is a simple random sample. We check for normality, which seems reasonable based on the normal quantile plot.

  13. Example - Continued Step 1: The claim that “the standard deviation is less than 2.6 inches” is expressed as σ < 2.6 inches. Step 2: If the original claim is false, then σ≥ 2.6 inches. Step 3: The hypotheses are:

  14. Example - Continued Step 4: The significance level is α = 0.01. Step 5: Because the claim is made about σ, we use the chi-square distribution.

  15. Example - Continued Step 6: The test statistic is calculated as follows: with 9 degrees of freedom.

  16. Example - Continued Step 6: The critical value of χ2 = 2.088 is found from Table A-4, and it corresponds to 9 degrees of freedom and an “area to the right” of 0.99.

  17. Example - Continued Step 7: Because the test statistic is in the critical region, we reject the null hypothesis. There is sufficient evidence to support the claim that supermodels have heights with a standard deviation that is less than 2.6 inches. Heights of supermodels have much less variation than heights of women in the general population.

  18. Example - Continued P-Value Method: P-values are generally found using technology, but Table A-4 can be used if technology is not available. Using a TI-83/84 Plus, the P-value is 0.0002897.

  19. Example - Continued P-Value Method: Since the P-value = 0.0002897, we can reject the null hypothesis (it is under the 0.01 significance level). We reach the same exact conclusion as before regarding the variation in the heights of supermodels as compared to the heights of women from the general population.

  20. Example - Continued Confidence Interval Method: Since the hypothesis test is left-tailed using a 0.01 level of significance, we can run the test by constructing an interval with 98% confidence. Using the methods of Section 7-4, and the critical values found in Table A-4, we can construct the following interval:

  21. Example - Continued Based on this interval, we can support the claim that σ < 2.6 inches, reaching the same conclusion as using the P-value method and the critical value method.

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