本书是一本优秀的统计模型教材,着重讲解线性模型的应用问题,包括广义最小二乘和两步最小二乘模型,以及二分变量的probit及logit模型的应用,还包括关于研究设计、二分变量回归及矩阵代数的背景知识。
这还是一本鼓舞人心的而又易读的书,无论是老师还是学生都会从中受益。
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书名 | 统计模型(理论和实践英文版第2版)/华章数学统计学原版精品系列 |
分类 | 经济金融-金融会计-会计 |
作者 | (美)弗里德曼 |
出版社 | 机械工业出版社 |
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简介 | 编辑推荐 本书是一本优秀的统计模型教材,着重讲解线性模型的应用问题,包括广义最小二乘和两步最小二乘模型,以及二分变量的probit及logit模型的应用,还包括关于研究设计、二分变量回归及矩阵代数的背景知识。 这还是一本鼓舞人心的而又易读的书,无论是老师还是学生都会从中受益。 目录 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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