A Handbook of Statistical Analyses Using Stata, 4th Edition

by Sophia Rabe-Hesketh and Brian Everitt

    The fourth edition of A Handbook of Statistical Analyses Using Stata, by Sophia Rabe-Hesketh and Brian Everitt, is now available and incorporates many of the features introduced in Stata 9. The book provides practical examples of using Stata for real-world analysis. It begins with a concise 41-page introduction to Stata and progresses to intermediate topics such as multiple regression, ANOVA, and logistic regression. Chapter-length discussions cover more advanced topics, such as the generalized linear model (GLM), survival analysis, maximum likelihood estimation, cluster analysis, and principal components analysis.

The most distinguishing feature of this text is its use of case studies to help users learn Stata’s capabilities in the various subfields of statistics, including generalized linear models, survival analysis, panel (longitudinal) data, and cluster analysis. For example, the discussion of survival analysis revolves around a study of methadone maintenance treatment to aid heroin addicts in overcoming their addiction.

A Handbook of Statistical Analysis Using Stata would make an excellent supplemental textbook in a course in statistics, allowing students to learn Stata on their own while reviewing concepts taught during lectures. The book will also help practitioners, particularly those in biostatistics and the health sciences, to become proficient with Stata quickly.

Table of contents

Preface

1 A Brief Introduction to Stata

1.1 Getting help and information
1.2 Running Stata
1.3 Conventions used in this book
1.4 Datasets in Stata
1.5 Stata commands
1.6 Data management
1.7 Estimation
1.8 Graphics
1.9 Stata as a calculator
1.10 Matrix calculations using Mata
1.11 Brief introduction to programming
1.12 Keeping Stata up to date
1.13 Exercises

2 Data Description and Simple Inference: Female Psychiatric Patients

2.1 Description of data
2.2 Group comparison and correlations
2.3 Analysis using Stata
2.4 Exercises

3 Multiple Regression: Determinants of Pollution in U.S. Cities

3.1 Description of data
3.2 The multiple regression model
3.3 Analysis using Stata
3.4 Exercises

4 Analysis of Variance I: Treating Hypertension

5 Analysis of Variance II: Effectiveness of Slimming Treatment

5.1 Description of data
5.2 Analysis of variance model
5.3 Analysis using Stata
Exercises

6 Logistic Regression: Treatment of Lung Cancer and Diagnosis of Heart Attacks

6.1 Description of data
6.2 The logistic regression model
6.3 Analysis using Stata
6.4 Exercises

7 Generalized Linear Models: Australian School Children

8 Summary Measure Analysis of Longitudinal Data: Treatment of Post-Natal Depression

8.1 Description of data
8.2 The analysis of longitudinal data
8.3 Analysis using Stata
8.4 Exercises

9 Random Effects Models: Thought disorder and schizophrenia

9.1 Description of data
9.2 Random effects models
9.3 Analysis using Stata
9.4 Thought disorder data
9.5 Exercises

10 Generalized Estimating Equations: Epileptic Seizures and Chemotherapy

10.1 Description of data
10.2 Generalized estimating equations
10.3 Analysis using Stata
10.4 Exercises

11 Some Epidemiology

11.1 Description of data
11.2 Introduction to epidemiology
11.3 Analysis using Stata
11.4 Exercises

Index

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