A Short Introduction to Stata 8 for Biostatistics

Hills

Reference

Stata Corp

Stata Longitudinal/panel Data Reference Manual. Release 9

Computers & Technology

Stata Press

Databases & Big Data

Mata Reference Manual: Release 9

Stata Multivariate Statistics Reference Manual

Stata Statistics/Graphs/Data Management Release 6 (Volume 1 Reference A-G)

Developing the statistical concepts unique to survival data, An Introduction to Survival Analysis Using Stata, Revised Edition includes statistical theory, step-by-step procedures for analyzing survival data, a detailed usage guide for Stata's most widely used st commands, and pointers for using Stata to analyze survival data and present the results. The first several chapters cover basic theoretical concepts as well as censoring and truncation.  The next few chapters describe the formatting, manipulation, stsetting, and error-checking involved in preparing survival data for analysis using Stata's st analysis commands. The book also discusses Cox regression and includes various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, and modeling diagnostics. The final chapters cover parametric models, which are fitted using Stata's streg command.

Introduction to Survival Analysis Using Stata

Business & Money

Cleves, Mario

Education & Reference

Cross-Sectional Time-Series Stata Release 8

STATA

Although regression models for categorical dependent variables are common, few texts explain how to interpret such models. Regression Models for Categorical Dependent Variables Using Stata, Second Edition, fills this void, showing how to fit and interpret regression models for categorical data with Stata. The authors also provide a suite of commands for hypothesis testing and model diagnostics to accompany the book. The book begins with an excellent introduction to Stata and then provides a general treatment of estimation, testing, fit, and interpretation in this class of models. It covers in detail binary, ordinal, nominal, and count outcomes in separate chapters. The final chapter discusses how to fit and interpret models with special characteristics, such as ordinal and nominal independent variables, interaction, and nonlinear terms. One appendix discusses the syntax of the author-written commands, and a second gives details of the datasets used by the authors in the book. Nearly 50% longer than the previous edition, the book covers new topics for fitting and interpreting models included in Stata 9, such as multinomial probit models, the stereotype logistic model, and zero-truncated count models. Many of the interpretation techniques have been updated to include interval as well as point estimates. New to the Second Edition: 
<li>Regression models, including the zero-truncated Poisson and the zero-truncated negative binomial models, the hurdle model for counts, the stereotype logistic regression model, the rank-ordered logit model, and the multinomial probit model 
</li>
<li>Stata commands, such as estat, which provides a uniform way to access statistics useful for postestimation interpretation. 
</li>
<li>Expanded suite of programs known as SPost 
</li>
<li>Inclusion of confidence intervals for predictions computed by prvalue and prgen Because all the examples, datasets, and author-written commands are available from the authors' Web site, readers can easily replicate the concrete examples using Stata, making it ideal for students or applied researchers who want to know how to fit and interpret models for categorical data.</li>

Regression Models for Categorical Dependent Variables Using Stata

Politics & Social Sciences

J. Scott Long

Social Sciences

Stata Base Reference Manual

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