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| （美）弗里德曼,是加州大学伯克利分校的统计学教授、杰出的数理统计学家。其研究范围包括鞅不等式分析、Markov过程、抽样、自助法等。他是美国科学学院士。在2003年。美国科学院授予他John J.Carty科学进步奖，以表彰他对统计理论和实践做出的贡献。 .. << 查看详细 |
| foreword to the revised edition preface 1 observational studies and experiments 1.1 introduction 1.2 the hip trial 1.3 snow on cholera 1.4 yule on the causes of poverty exercise set a 1.5 end notes 2 the regression line 2.1 introduction 2.2 the regression line 2.3 hooke\'s law exercise set a 2.4 complexities 2.5 simple vs multiple regression exercise set b 2.6 end notes 3 matrix algebra 3.1 introduction . exercise set a 3.2 determinants and inverses exercise set b 3.3 random vectors exercise set c 3.4 positive definite matrices exercise set d 3.5 the normal distribution exercise set e 3.6 if you want a book on matrix algebra 4 multiple regression 4.1 introduction exercise set a 4.2 standard errors things we don\'t need exercise set b 4.3 explained variance in multiple regression association or causation? exercise set c 4.4 what happens to ols if the assumptions break down? 4.5 discussion questions 4.6 end notes 5 multiple regression: special topics 5.1 introduction 5.2 olsisblue exercise set a 5.3 generalized least squares exercise set b 5.4 examples on gls exercise set c 5.5 what happens to gls if the assumptions break down? 5.6 normal theory statistical significance exercise set d 5.7 the f-test "the" f-test in applied work exercise set e 5.8 data snooping exercise set f 5.9 discussion questions 5.10 end notes 6 path models 6.1 stratification exercise set a 6.2 hooke\'s law revisited exercise set b 6.3 political repression during the mccarthy era exercise set c 6.4 inferring causation .by regression exercise set d 6.5 response schedules for path diagrams selection vs intervention structural equations and stable parameter:ambiguity in notation exercise set e 6.6 dummy variables types of variables 6.7 discussion questions 6.8 end notes 7 maximum likelihood 7.1 introduction exercise set a 7.2 probit models why not regression? the latent-variable formulation exercise set b identification vs estimation what if the ui are n? exercise set c 7.3 logit models exercise set d 7.4 the effect of catholic schools latent variables response schedules the second equation mechanics: bivariate probit why a model rather than a cross-lab? interactions more on table 3 in evans and schwab more on the second equation exercise set e 7.5 discussion questions 7.6 end notes 8 the bootstrap 8.1 introduction exercise set a 8.2 bootstrapping a model for energy demand exercise set b 8.3 end notes 9 simultaneous equations 9.1 introduction exercise set a 9.2 instrumental variables exercise set b 9.3 estimating the butter model exercise set c 9.4 what are the two stages? invariance assumptions 9.5 a social-science example: education and fertility more on rindfuss et al 9.6 covariates 9.7 linear probability models the assumptions the questions exercise set d 9.8 more on ivls some technical issues exercise set e simulations to illustrate ivls 9.9 discussion questions 9.10 end notes 10 issues in statistical modeling 10.1 introduction the bootstrap the role of asymptotics philosophers\' stones the modelers\' response 10.2 critical literature 10.3 response schedules 10.4 evaluating the models in chapters 7-9 10.5 summing up references answers to exercises the computer labs appendix: sample matlab code reprints gibson on mccarthy evans and schwab on catholic schools rindfuss et al on education and fertility schneider et al on social capital index |
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