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Chapter 22 Comparing 2 Proportions © 2006 W.H. Freeman and Company

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Objectives (Chapter 22) Comparing two proportions Comparing two independent samples Large-sample CI for two proportions Test of statistical significance

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Comparing two independent samples We often need to estimate the difference p 1 – p 2 between two unknown population proportions based on independent samples. We can compute the difference between the two sample proportions and compare it to the corresponding, approximately normal sampling distribution for

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4 Sample 1 Sample size n 1 Number of successes x 1 Sample proportion Sample 1 Sample size n 1 Number of successes x 1 Sample proportion Two random samples are drawn from two populations. The number of successes in each sample is recorded. The sample proportions are computed. Sample 2 Sample size n 2 Number of successes x 2 Sample proportion Sample 2 Sample size n 2 Number of successes x 2 Sample proportion x n 1 1 ˆ p 1 Point Estimator of p 1 – p 2 :

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Large-sample CI for two proportions For two independent SRSs of sizes n 1 and n 2 with sample proportion of successes 1 and 2 respectively, an approximate level C confidence interval for p 1 – p 2 is Use this method when C is the area under the standard normal curve between −z* and z*.

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Cholesterol and heart attacks How much does the cholesterol-lowering drug Gemfibrozil help reduce the risk of heart attack? We compare the incidence of heart attack over a 5-year period for two random samples of middle-aged men taking either the drug or a placebo. So the 90% CI is (0.0414 − 0.0273) ± 1.645*0.00746 = 0.0141 ± 0.0125 = (0.016, 0.0266) We are 90% confident that the interval 0.16% to 2.66% captures the true percentage difference in heart attack rates for middle-aged men when taking a placebo and the cholesterol-lowering drug. Standard error of the difference p 1 − p 2 : H. attackn Drug p 2 5620512.73% Placebo p 1 8420304.14%

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7 Example: 95% confidence interval for p 1 – p 2 The age at which a woman gives birth to her first child may be an important factor in the risk of later developing breast cancer. An international study conducted by WHO selected women with at least one birth and recorded if they had breast cancer or not and whether they had their first child before their 30 th birthday or after. CancerSample Size Age at First Birth > 30 683322021.2% Age at First Birth <= 30 149810,24514.6% The parameter to be estimated is p1 – p2. p1 = cancer rate when age at 1 st birth >30 p2 = cancer rate when age at 1 st birth <=30 We estimate that the cancer rate when age at first birth > 30 is between.05 and.082 higher than when age <= 30.

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If the null hypothesis is true, then we can rely on the properties of the sampling distribution of to estimate the probability of selecting 2 samples with proportions HypothesisTests for p 1 – p 2 This test is appropriate when =0

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Gastric Freezing Gastric freezing was once a treatment for ulcers. Patients would swallow a deflated balloon with tubes, and a cold liquid would be pumped for an hour to cool the stomach and reduce acid production, thus relieving ulcer pain. The treatment was shown to be safe, significantly reducing ulcer pain, and was widely used for years. A randomized comparative experiment later compared the outcome of gastric freezing with that of a placebo: 28 of the 82 patients subjected to gastric freezing improved, while 30 of the 78 in the control group improved. Conclusion: The gastric freezing was no better than a placebo (P-value 0.69), and this treatment was abandoned. ALWAYS USE A CONTROL! H 0 : p gf - p placebo = 0 H a : p gf - p placebo > 0 p gf = proportion that receive relief from gastric freezing p placebo = proportion that receive relief using a placebo

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