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Independent Samples: Comparing Proportions

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## Independent Samples: Comparing Proportions

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**Independent Samples: Comparing Proportions**Lecture 41 Section 11.5 Mon, Nov 21, 2005**Comparing Proportions**• We now wish to compare proportions between two populations. • Normally, we would be measuring proportions for the same attribute. • For example, we could measure the proportion of NC residents living below the poverty level and the proportion of VA residents living below the poverty level.**Examples**• The “gender gap” – the proportion of men who vote Republican vs. the proportion of women who vote Republican. • The proportion of teenagers who smoked marijuana in 1995 vs. the proportion of teenagers who smoked marijuana in 2000.**Examples**• The proportion of patients who recovered, given treatment A vs. the proportion of patients who recovered, given treatment B. • Treatment A could be a placebo.**Comparing proportions**• To estimate the difference between population proportions p1 and p2, we need the sample proportions p1^ and p2^. • The difference p1^ – p2^ is an estimator of the difference p1 – p2.**Hypothesis Testing**• See Example 11.8, p. 721 – Perceptions of the U.S.: Canadian versus French. • p1 = proportion of Canadians who feel positive about the U.S.. • p2 = proportion of French who feel positive about the U.S..**The hypotheses.**• H0: p1 – p2 = 0 (i.e., p1 = p2) • H1: p1 – p2 > 0 (i.e., p1 > p2) • The significance level is = 0.05. • What is that test statistic? • That depends on the sampling distribution of p1^ – p2^.**The Sampling Distribution of p1^ – p2^**• If the sample sizes are large enough, then p1^ is N(p1, 1), where • Similarly, p2^ is N(p2, 2), where**The Sampling Distribution of p1^ – p2^**• Therefore, where**The Sampling Distribution of p1^ – p2^**• The sample sizes will be large enough if • n1p1 5, and n1(1 – p1) 5, and • n2p2 5, and n2(1 – p2) 5.**The Test Statistic**• Therefore, the test statistic would be if we knew the values of p1 and p2. • We could estimate them with p1^ and p2^. • But there is a better way…**Pooled Estimate of p**• In hypothesis testing for the difference between proportions, typically the null hypothesis is H0: p1 = p2 • Under that assumption, p1^ and p2^ are both estimators of a common value (call it p).**Pooled Estimate of p**• Rather than use either p1^ or p2^ alone to estimate p, we will use a “pooled” estimate. • That is the proportion that we would get if we pooled the two samples together.**The Standard Deviation of p1^ – p2^**• This leads to a better estimator of the standard deviation of p1^ – p2^.**Caution**• If the null hypothesis does not say H0: p1 = p2 then we should not use the pooled estimate p^, but should use the unpooled estimate**The Test Statistic**• So the test statistic is**The Value of the Test Statistic**• Compute p^: • Now compute z:**The p-value, etc.**• Compute the p-value: P(Z > 7.253) = 2.059 10-13. • Reject H0. • The data indicate that a greater proportion of Canadians than French have a positive feeling about the U.S.**Exercise 11.34**• Sample #1: 361 “Wallace” cars reveal that 270 have the sticker. • Sample #2: 178 “Humphrey” cars reveal that 154 have the sticker. • Do these data indicate that p1 p2?**Example**• State the hypotheses. • H0: p1 = p2 • H1: p1p2 • State the level of significance. • = 0.05.**Example**• Write the test statistic.**Example**• Compute p1^, p2^, and p^.**Example**• Now we can compute z.**Example**• Compute the p-value. • p-value = 2 normalcdf(-E99, -3.126) = 2(0.0008861) = 0.001772. • The decision is to reject H0. • State the conclusion. • The data indicate that the proportion of Wallace cars that have the sticker is different from the proportion of Humphrey cars that have the sticker.**TI-83 – Testing Hypotheses Concerning p1^ – p2^**• Press STAT > TESTS > 2-PropZTest... • Enter • x1, n1 • x2, n2 • Choose the correct alternative hypothesis. • Select Calculate and press ENTER.**TI-83 – Testing Hypotheses Concerning p1^ – p2^**• In the window the following appear. • The title. • The alternative hypothesis. • The value of the test statistic z. • The p-value. • p1^. • p2^. • The pooled estimate p^. • n1. • n2.**Example**• Work Exercise 11.34 using the TI-83.**Confidence Intervals for p1^ – p2^**• The formula for a confidence interval for p1^ – p2^ is • Caution: Note that we do not use the pooled estimate for p^.**TI-83 – Confidence Intervals for p1^ – p2^**• Press STAT > TESTS > 2-PropZInt… • Enter • x1, n1 • x2, n2 • The confidence level. • Select Calculate and press ENTER.**TI-83 – Confidence Intervals for p1^ – p2^**• In the window the following appear. • The title. • The confidence interval. • p1^. • p2^. • n1. • n2.**Example**• Find a 95% confidence interval using the data in Exercise 11.34.