1. BPS - 3rd Ed. Chapter 191 Comparing Two Proportions

2. BPS - 3rd Ed. 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. u Each sample is separate. The units are not matched, and the samples can be of differing sizes.

3. BPS - 3rd Ed. 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. BPS - 3rd Ed. 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. BPS - 3rd Ed. Chapter 195 Inference about the Difference p 1 – p 2 Sampling Distribution

6. BPS - 3rd Ed. 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. BPS - 3rd Ed. Chapter 197

8. BPS - 3rd Ed. 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. BPS - 3rd Ed. Chapter 199 u The standard confidence interval approach yields unstable or erratic inferences. u By adding four imaginary observations (one success and one failure to each sample), the inferences can be stabilized. u This leads to more accurate inference of the difference of the population proportions. Adjustment to Confidence Interval “Plus Four” Confidence Interval for p 1 – p 2

10. BPS - 3rd Ed. Chapter 1910 Adjustment to Confidence Interval “Plus Four” Confidence Interval for p 1 – p 2 u Add 4 imaginary observations, one success and one failure to each sample. u Compute the “plus four” proportions. u Use the “plus four” proportions in the formula.

11. BPS - 3rd Ed. Chapter 1911 Case Study: Reliability “Plus Four” 90% Confidence Interval We are 90% confident that the difference in proportion of defectives for the two machines is between -3.94% and 4.74%. Since 0 is in this interval, it is unlikely that the two machines differ in reliability. (This is more accurate.)

12. BPS - 3rd Ed. Chapter 1912 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

13. BPS - 3rd Ed. Chapter 1913 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

14. BPS - 3rd Ed. Chapter 1914 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:

15. BPS - 3rd Ed. Chapter 1915 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.

16. BPS - 3rd Ed. Chapter 1916

17. BPS - 3rd Ed. Chapter 1917 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

18. BPS - 3rd Ed. Chapter 1918 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

19. BPS - 3rd Ed. Chapter 1919 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

20. BPS - 3rd Ed. Chapter 1920 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.