1. Essential Statistics Chapter 191 Comparing Two Proportions

2. Essential Statistics Chapter 192 Two-Sample Problems u The goal of inference is to compare the responses to two treatments or to compare the characteristics of two populations. u We have a separate sample from each treatment or each population. The units are not matched, and the samples can be of differing sizes.

3. Essential Statistics Chapter 193 Case Study A study is performed to test of the reliability of products produced by two machines. Machine A produced 8 defective parts in a run of 140, while machine B produced 10 defective parts in a run of 200. This is an example of when to use the two-proportion z procedures. nDefects Machine A1408 Machine B20011 Machine Reliability

4. Essential Statistics Chapter 194 Inference about the Difference p 1 – p 2 Simple Conditions u The difference in the population proportions is estimated by the difference in the sample proportions: u When both of the samples are large, the sampling distribution of this difference is approximately Normal with mean p 1 – p 2 and standard deviation

5. Essential Statistics Chapter 195 Inference about the Difference p 1 – p 2 Sampling Distribution

6. Essential Statistics Chapter 196 Since the population proportions p 1 and p 2 are unknown, the standard deviation of the difference in sample proportions will need to be estimated by substituting for p 1 and p 2 : Standard Error

7. Essential Statistics Chapter 197

8. Essential Statistics Chapter 198 Case Study: Reliability We are 90% confident that the difference in proportion of defectives for the two machines is between -3.97% and 4.39%. Since 0 is in this interval, it is unlikely that the two machines differ in reliability. Compute a 90% confidence interval for the difference in reliabilities (as measured by proportion of defectives) for the two machines. Confidence Interval

9. Essential Statistics Chapter 199 The Hypotheses for Testing Two Proportions u Null: H 0 : p 1 = p 2 u One sided alternatives H a : p 1 > p 2 H a : p 1 < p 2 u Two sided alternative H a : p 1  p 2

10. Essential Statistics Chapter 1910 Pooled Sample Proportion u If H 0 is true (p 1 =p 2 ), then the two proportions are equal to some common value p. u Instead of estimating p 1 and p 2 separately, we will combine or pool the sample information to estimate p. u This combined or pooled estimate is called the pooled sample proportion, and we will use it in place of each of the sample proportions in the expression for the standard error SE. pooled sample proportion

11. Essential Statistics Chapter 1911 Test Statistic for Two Proportions u Use the pooled sample proportion in place of each of the individual sample proportions in the expression for the standard error SE in the test statistic:

12. Essential Statistics Chapter 1912 P-value for Testing Two Proportions u H a : p 1 > p 2 v P-value is the probability of getting a value as large or larger than the observed test statistic (z) value. u H a : p 1 < p 2 v P-value is the probability of getting a value as small or smaller than the observed test statistic (z) value. u H a : p 1 ≠ p 2 v P-value is two times the probability of getting a value as large or larger than the absolute value of the observed test statistic (z) value.

13. Essential Statistics Chapter 1913

14. Essential Statistics Chapter 1914 Case Study u A university financial aid office polled a simple random sample of undergraduate students to study their summer employment. u Not all students were employed the previous summer. Here are the results: u Is there evidence that the proportion of male students who had summer jobs differs from the proportion of female students who had summer jobs. Summer StatusMenWomen Employed718593 Not Employed79139 Total797732 Summer Jobs

15. Essential Statistics Chapter 1915 u Null: The proportion of male students who had summer jobs is the same as the proportion of female students who had summer jobs. [H 0 : p 1 = p 2 ] u Alt: The proportion of male students who had summer jobs differs from the proportion of female students who had summer jobs. [H a : p 1 ≠ p 2 ] The Hypotheses Case Study: Summer Jobs

16. Essential Statistics Chapter 1916 Case Study: Summer Jobs u n 1 = 797 and n 2 = 732 (both large, so test statistic follows a Normal distribution) u Pooled sample proportion: u standardized score (test statistic): Test Statistic

17. Essential Statistics Chapter 1917 Case Study: Summer Jobs 1. Hypotheses:H 0 : p 1 = p 2 H a : p 1 ≠ p 2 2. Test Statistic: 3. P-value: P-value = 2P(Z > 5.07) = 0.000000396 (using a computer) P-value = 2P(Z > 5.07) 3.49 (the largest z-value in the table)] 4. Conclusion: Since the P-value is smaller than  = 0.001, there is very strong evidence that the proportion of male students who had summer jobs differs from that of female students.