世界环境保护英文.pptVIP

Comparing Two Population Proportions Goal: Compare two populations/treatments wrt a nominal (binary) outcome Sampling Design: Independent vs Dependent Samples Methods based on large vs small samples Contingency tables used to summarize data Measures of Association: Absolute Risk, Relative Risk, Odds Ratio Contingency Tables Tables representing all combinations of levels of explanatory and response variables Numbers in table represent Counts of the number of cases in each cell Row and column totals are called Marginal counts 2x2 Tables - Notation Example - Firm Type/Product Quality Notation Proportion in Population 1 with the characteristic of interest: p1 Sample size from Population 1: n1 Number of individuals in Sample 1 with the characteristic of interest: X1 Sample proportion from Sample 1 with the characteristic of interest: Similar notation for Population/Sample 2 Example - Cotton Textile Producers p1 - True proportion of all Non-integretated firms that would produce High quality p2 - True proportion of all vertically integretated firms that would produce High quality Notation (Continued) Parameter of Primary Interest: p1-p2, the difference in the 2 population proportions with the characteristic (2 other measures given below) Estimator: Standard Error (and its estimate): Pooled Estimated Standard Error when p1=p2=p: Cotton Textile Producers (Continued) Parameter of Primary Interest: p1-p2, the difference in the 2 population proportions that produce High quality output Estimator: Standard Error (and its estimate): Pooled Estimated Standard Error when p1=p2=p: Confidence Interval for p1-p2 (Wilson’s Estimate) Method adds a success and a failure to each group to improve the coverage rate under certain conditions: The confidence interval is of the form: * 起重机 立体停车设备 电动葫芦 起重机 立体停车设备 电动葫芦 n1+n2 (n1+n2)-(X1+X2) X1+X2 Outcome Total n2 n2-X2 X2 Group 2 n1 n1-X1 X1 Group 1 Group Total Outcome Absent Outcome Present 172 134 38 Outcome Total 84 79