Handbook of Statistical Analyses Using Stata, Second Edition

Handbook of Statistical Analyses Using Stata, Second Edition

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Stata - the powerful statistical software package - has streamlined data analysis, interpretation, and presentation for researchers and statisticians around the world. Because of its power and extensive features, however, the Stata manuals are quite detailed and extensive.

The Second Edition of A Handbook of Statistical Analyses using Stata describes the features of the latest version of Stata - Version 6 - in a concise, convenient format. It gives new users a head start on using the program and offers experienced users a quick reference. The authors illustrate how Stata can be used to apply a variety of statistical techniques, including regression, survival analysis, and principal components. They outline Stata's value in:

modeling complex data from longitudinal studies or surveys

analyzing results from clinical trials or epidemiological studies

using its powerful programming language to apply tailor-made analyses relatively simply

Each chapter deals with a particular data set, identifies the appropriate analysis for it, and includes a brief account of the statistical background of the technique applied. However, the primary focus is on using Stata 6 and interpreting results. As such, this text complements the Stata manuals, offering both an ideal introduction to the program and a handy reference - perfect as a supplementary text for a second course in statistics.
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Product details

  • Paperback | 224 pages
  • 144.8 x 223.5 x 15.2mm | 294.84g
  • Chapman & Hall/CRC
  • United States
  • English, Chinese
  • New edition
  • 2nd New edition
  • 16 Tables, black and white
  • 1584882018
  • 9781584882015

Table of contents

A Brief Introduction to Stata
Data Description and Simple Inference
Multiple Regression
Analysis of Variance I
Analysis of Variance II
Logistic Regression
Generalized Linear Models
Analysis of Longitudinal Data I
Analysis of Longitudinal Data II
Some Epidemiology
Survival Analysis
Principal Components Analysis
Maximum Likelihood Estimation
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