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[下载]Introduction to Stata 8 [推广有奖]

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hanszhu 发表于 2005-4-12 05:34:00 |显示全部楼层
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关键词:introduction troduction intro Stata tata 下载 Stata introduction

hanszhu 发表于 2005-4-12 07:43:00 |显示全部楼层

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hanszhu 发表于 2005-4-12 08:02:00 |显示全部楼层

Resources based on Stata 7

Getting Started with Stata for MS Windows: A Brief Introduction, Robert Yaffee, New York University, USA
An introduction to Stata in PDF format.
e-Tutorial on Stata, UIUC, USA
Electronic notes and tutorials by the teaching assistant of an applied econometrics course focusing on the basics of Stata and R.
Introducing Stata: A Statistical Program For Socio-economic Analysis, Peter Gruhn, Agrifood Consulting International
Training material prepared to introduce researchers in Nepal to the analysis of socioeconomic data using Stata.
Stata Tutorial, University of Essex
Tutorial on basic procedures in Stata.

[此贴子已经被作者于2005-4-12 8:11:52编辑过]

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hanszhu 发表于 2005-4-12 08:03:00 |显示全部楼层

Resources based on Stata 9

Resources to help you learn and use Stata, UCLA Academic Technology Services, USA
An extensive resource of Stata information, including FAQs, learning modules, a quick-reference guide, annotated output, textbook examples, and more. New users may want to visit the Stata Starter Kit section of the UCLA site. Don't miss the Stata Web Books and the movies.
SSC Archive, Boston College, USA
A complete archive of programs exchanged on the Stata listserver and other programs for Stata. All these programs can also be installed directly from within Stata by typing net from http://fmwww.bc.edu/RePEc/bocode/ at the Stata prompt or use the ssc command.
Site includes full search capabilities.
UCLA Stata Portal, UCLA Academic Technology Services, USA
A web site that links and searches across Stata sites around the world, and more. This is a place where developers of Stata resources collaborate to create an infrastructure to help their own Stata community while providing a service to Stata users all over the world.

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hanszhu 发表于 2005-4-12 08:04:00 |显示全部楼层

Resources based on Stata 8

Resources to help you learn and use Stata, UCLA Academic Technology Services, USA
An extensive resource of Stata information, including FAQs, learning modules, a quick-reference guide, annotated output, textbook examples, and more. New users may want to visit the Stata Starter Kit section of the UCLA site. Don't miss the Stata Web Books and the movies.
South Africa Distance Learning Project on Stata, University of Michigan, USA
An extensive teaching web site dedicated to Stata. Some of the topics covered are
  • Introduction to surveys
  • Understanding distributions
  • Multiple-regression analysis
  • Graphing with Stata 8
Survival Analysis with Stata: Course EC968, Stephen Jenkins, Institute for Social and Economic Research, University of Essex, UK
Lessons, programs, do-files, and a PDF book about survival analysis in Stata.
Topics in Economic Analysis – Stata course, London School of Economics, UK
Class notes, tutorials, sample data, and do-files from an economics course at the London School of Economics. In particular, see the excellent Introduction to Stata.
An Introduction to Stata (pdf), IT Support at the LSE Research Laboratory, UK
An introduction to Stata and various commands.
Introduction to Stata 8, Svend Juul, Department of Epidemiology and Social Medicine, University of Aarhus, Denmark
A 72-page introduction describing the basics of Stata 8, including a guide to using the new Stata 8 graphics.
Graduate statistics lecture notes, Richard Williams, Sociology Department, University of Notre Dame
Extensive lecture notes covering applied statistical topics from probability distribution through logistic regression, with examples using Stata.
Applied Econometrics lecture notes, Carlos Lamarche, Econometrics Group, University of Illinois at Urbana-Champaign
Lecture notes covering introductory econometrics topics, including Box-Cox transformation, dynamic models, bootstrapping techniques, Granger causality, Monte Carlo simulation and nonlinear regression, and simultaneous equation models, with examples using Stata.
Stata Introduction, Princeton University, USA
An extensive introduction to Stata covering general information about Stata and for learning Stata.
Introduction to Stata 8 (pdf), Christopher F. Baum, Boston College, USA
A 67-page description of Stata, its key features and benefits and other useful information.
LAB1: Introduction to working with Stata 8 (pdf), Karolinska Institutet, Sweden
A brief introduction, including course notes and exercises, on handling data in Stata.
Introduction to Stata (pdf), Dr. Joachim Winter, Universität Mannheim, Germany
A brief overview of the Stata interface, command syntax, and capabilities.
An Introduction to Stata: Part I (pdf), Thomas Lumley, University of Washington, USA
An introduction to Stata, including basic commands and features, for a biostatistics course.
A Quick Guide to Stata 8 for Windows (pdf), Kurt Schmidheiny, Université de Lausanne, HEC, Switzerland
Course notes on the basics of Stata, including important features and functions.
Data management tutorial, Carolina Population Center, University of North Carolina at Chapel Hill, USA
Tutorial, examples, and question-and-answer sessions on managing data in Stata, with an emphasis on survey data.
UCLA Stata Portal, UCLA Academic Technology Services, USA
A web site that links and searches across Stata sites around the world, and more. This is a place where developers of Stata resources collaborate to create an infrastructure to help their own Stata community while providing a service to Stata users all over the world.
Stata tutorials for the American Economic Association Summer Program 2004, Duke University in partnership with North Carolina A&T State University
Class notes, tutorials, and do-files by Stas Kolenikov for the American Economic Association Summer Program at Duke University.

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hanszhu 发表于 2005-4-12 08:45:00 |显示全部楼层

[下载]Ebook.Stata Press.Regression Models For Categorical Dependent Variables

12042.rar (2.63 MB, 需要: 50 个论坛币)

[此贴子已经被作者于2005-4-12 11:23:57编辑过]

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hanszhu 发表于 2005-4-12 09:07:00 |显示全部楼层

Regression Models for Categorical Dependent Variables using Stata

Part I General Information

1 Introduction

1.1 What is this book about?
1.2 Which models are considered?
1.3 Who is this book for?
1.4 How is the book organized?
1.5 What software do you need?
1.5.1 Updating Stata 8
1.5.2 Installing SPost
Installing SPost using net search
Installing SPost using net install
1.5.3 What if commands do not work?
1.5.4 Uninstalling SPost
1.5.5 Additional files available on the web site
1.6 Where can I learn more about the models?
2 Introduction to Stata
2.1 The Stata interface
Changing the scrollback buffer size
Changing the display of variable names in the Variables window
2.2 Abbreviations
2.3 How to get help
2.3.1 Online help
2.3.2 Manuals
2.3.3 Other resources
2.4 The working directory
2.5 Stata file types
2.6 Saving output to log files
Options
2.6.1 Closing a log file
2.6.2 Viewing a log file
2.6.3 Converting from SMCL to plain text or PostScript
2.7 Using and saving datasets
2.7.1 Data in Stata format
2.7.2 Data in other formats
2.7.3 Entering data by hand
2.8 Size limitations on datasets
2.9 do-files
2.9.1 Adding comments
2.9.2 Long lines
2.9.3 Stopping a do-file while it is running
2.9.4 Creating do-files
Using Stata's do-file editor
Using other editors to create do-files
2.9.5 A recommended structure for do-files
2.10 Using Stata for serious data analysis
2.11 The syntax of Stata commands
2.11.1 Commands
2.11.2 Variable lists
2.11.3 if and in qualifiers
Examples of if qualifier
2.11.4 Options
2.12 Managing data
2.12.1 Looking at your data
2.12.2 Getting information about variables
2.12.3 Missing values
2.12.4 Selecting observations
2.12.5 Selecting variables
2.13 Creating new variables
2.13.1 generate command
2.13.2 replace command
2.13.3 recode command
2.13.4 Common transformations for RHS variables
Breaking a categorical variable into a set of binary variables
More examples of creating binary variables
Nonlinear transformations
Interaction terms
2.14 Labeling variables and values
2.14.1 Variable labels
2.14.2 Value labels
2.14.3 notes command
2.15 Global and local macros
2.16 Graphics
2.16.1 The graph command
2.16.2 Displaying previously drawn graphs
2.16.3 Printing graphs
2.16.4 Combining graphs
2.17 A brief tutorial
A batch version

3 Estimation, Testing, Fit, and Interpretation
3.1 Estimation
3.1.1 Stata's output for ML estimation
3.1.2 ML and sample size
3.1.3 Problems in obtaining Ml estimates
3.1.4 The syntax of estimation commands
Variable lists
Specifying the estimation sample
Options
3.1.5 Reading the output
Header
Estimates and standard errors
3.1.6 Reformatting output with estimates table
3.1.7 Reformatting output with outreg
3.1.8 Alternative output with listcoef
Options for types of coefficients
Other options
Standardized coefficients
Factor and percent change
3.1.9 Storing estimation results
3.2 Post-estimation analysis
3.3 Testing
3.3.1 Wald tests
The accumulate option
3.3.2 LR tests
Avoiding invalid LR tests
3.4 Measures of fit
Syntax of fitstat
Options
Models and measures
Example of fitstat
Methods and formulas for fitstat
3.5 Interpretation
3.5.1 Approaches to interpretation
3.5.2 Predictions using predict
3.5.3 Overview of prchange, prgen, prtab, and prvalue
Specifying the levels of variables
Options for controlling output
3.5.4 Syntax for prchange
Options
3.5.5 Syntax for prgen
Options
Variables generated
3.5.6 Syntax for prtab
Options
3.5.7 Syntax for prvalue
Options
3.5.8 Computing marginal effects using mfx compute
3.6 Next steps

Part II Models for Specific Kinds of Outcomes

4 Models for Binary Outcomes
4.1 The statistical model
4.1.1 A latent variable model
4.1.2 A nonlinear probability model
4.2 Estimation using logit and probit
Variable lists
Specifying the estimation sample
Weights
Options
Example
4.2.1 Observations predicted perfectly
4.3 Hypothesis testing with test and lrtest
4.3.1 Testing individual coefficients
One and two-tailed tests
Testing single coefficients using test
Testing single coefficients using lrtest
4.3.2 Testing multiple coefficients
Testing multiple coefficients using test
Testing multiple coefficients using lrtest
4.3.3 Comparing LR and Wald tests
4.4 Residuals and influence using predict
4.4.1 Residuals
Example
4.4.2 Influential cases
4.5 Scalar measures of fit using fitstat
Example
4.6 Interpretation using predicted values
4.6.1 Predicted probabilities with predict
4.6.2 Individual predicted probabilities with prvalue
4.6.3 Tables of predicted probabilities with prtab
4.6.4 Graphing predicted probabilities with prgen
4.6.5 Changes in predicted probabilities
Marginal change
Discrete change
4.7 Interpretation using odds ratios with listcoef
Multiplicative coefficients
Effect of the base probability
Percent change in the odds
4.8 Other commands for binary outcomes

5 Models for Ordinal Outcomes
5.1 The statistical model
5.1.1 A latent variable model
5.1.2 A nonlinear probability model
5.2 Estimation using ologit and oprobit
Variable lists
Specifying the estimation sample
Weights
Options
5.2.1 Example of attitudes toward working mothers
5.2.2 Predicting perfectly
5.3 Hypothesis testing with test and lrtest
5.3.1 Testing individual coefficients
5.3.2 Testing multiple coefficients
5.4 Scalar measures of fit using fitstat
5.5 Converting to a different parameterization
5.6 The parallel regression assumption
5.7 Residuals and outliers using predict
5.8 Interpretation
5.8.1 Marginal change in y
5.8.2 Predicted probabilities
5.8.3 Predicted probabilities with predict
5.8.4 Individual predicted probabilities with prvalue
5.8.5 Tables of predicted probabilities with prtab
5.8.6 Graphing predicted probabilities with prgen
5.8.7 Changes in predicted probabilities
Marginal change with prchange
Marginal change with mfx compute
Discrete change with prchange
Computing discrete change for a 10-year increase in age
Odds ratios using listcoef
5.8.8 Odds ratios using listcoef
5.9 Less-common models for ordinal outcomes
5.9.1 Generalized ordered logit model
5.9.2 The stereotype model
5.9.3 The continuation ratio model

6 Models for Nominal Outcomes
6.1 The multinomial logit model
6.1.1 Formal statement of the model
6.2 Estimation using mlogit
Variable lists
Specifying the estimation sample
Weights
Options
6.2.1 Example of occupational attainment
6.2.2 Using different base categories
6.2.3 Predicting perfectly
6.3 Hypothesis testing of coefficients
6.3.1 mlogtest for tests of the MNLM
Options
6.3.2 Testing the effects of the independent variables
A likelihood-ratio test
A Wald test
Testing multiple independent variables
6.3.3 Tests for combining dependent categories
A Wald test for combining outcomes
Using test [category]
An LR test for combining outcomes
Using constraint with lrtest
6.4 Independence of irrelevant alternatives
Hausman test of IIA
Small and Hsiao test of IIA
Conclusions regarding tests of IIA
6.5 Measures of fit
6.6 Interpretation
6.6.1 Predicted probabilities
6.6.2 Predicted probabilities with predict
Using predict to compare mlogit and ologit
6.6.3 Individual predicted probabilities with prvalue
6.6.4 Tables of predicted probabilities with prtab
6.6.5 Graphing predicted probabilities with prgen
Plotting probabilities for one outcome and two groups
Graphing probabilities for all outcomes for one group
6.6.6 Changes in predicted probabilities
Computing marginal and discrete change with prchange
Marginal change with mfx compute
6.6.7 Plotting discrete changes with prchange and mlogview
6.6.8 Odds ratios using listcoef and mlogview
Listing odds ratios with listcoef
Plotting odds ratios
6.6.9 Using mlogplot
6.6.10 Plotting estimates from matrices with mlogplot
Options for using matrices with mlogplot
Global macros and matrices used by mlogplot
Example
6.7 The conditional logit model
6.7.1 Data arrangement for conditional logit
6.7.2 Fitting the conditional logit model
Options
Example of the clogit model
6.7.3 Interpreting results from clogit
Using odds ratios
Using predicted probabilities
6.7.4 Fitting the multinomial logit model using clogit
Setting up the data
Creating interactions
Fitting the model
6.7.5 Using clogit to fit mixed models

7 Models for Count Outcomes
7.1 The Poisson distribution
7.1.1 Fitting the Poisson distribution with the poisson command
7.1.2 Computing predicted probabilities with prcounts
Syntax
Options
Variables generated
7.1.3 Comparing observed and predicted counts with prcounts
7.2 The Poisson regression model
7.2.1 Estimating the PRM with poisson
Variable lists
Specifying the estimation sample
Weights
Options
7.2.2 Example of fitting the PRM
7.2.3 Interpretation using the rate µ
Factor change in E(y|x)
Percent change in E(y|x)
Example of factor and percent change
Marginal change in E(y|x)
Example of marginal change using prchange
Example of marginal change using mfx compute
Discrete change in E(y|x)
Example of discrete change using prchange
7.2.4 Interpretation using predicted probabilities
Example of predicted probabilities using prvalue
Example of predicted probabilities using prgen
Example of predicted probabilities using prcounts
7.2.5 Exposure time
7.3 The negative binomial regression model
7.3.1 Fitting the NBRM with nbreg
7.3.2 Example of fitting the NBRM
Comparing the PRM and NBRM using estimates table
7.3.3 Testing for overdispersion
7.3.4 Interpretation using the rate µ
7.3.5 Interpretation using predicted probabilities
7.4 Zero-inflated count models
7.4.1 Estimation of zero-inflated models with zinb and zip
Variable lists
Options
7.4.2 Example of fitting the ZIP and ZINB models
7.4.3 Interpretation of coefficients
7.4.4 Interpretation of predicted probabilities
Predicted probabilities with prvalue
Predicted probabilities with prgen
7.5 Comparisons among count models
7.5.1 Comparing mean probabilities
7.5.2 Tests to compare count models
LR tests of a
Vuong test non-nested models

8 Additional Topics
8.1 Ordinal and nominal independent variables
8.1.1 Coding a categorical independent variable as a set of dummy variables
8.1.2 Estimation and interpretation with categorical independent variables
8.1.3 Tests with categorical independent variables
Testing the effect of membership in one category versus the reference category
Testing the effect of membership in two nonreference categories
Testing that a categorical independent variable has no effect
Testing whether treating an ordinal variable as interval loses information
8.1.4 Discrete change for categorical independent variables
Computing discrete change with prchange
Computing discrete change with prvalue
8.2 Interactions
8.2.1 Computing gender differences in predictions with interactions
8.2.2 Computing gender differences in discrete change with interactions
8.3 Nonlinear nonlinear models
8.3.1 Adding nonlinearities to linear predictors
8.3.2 Discrete change in nonlinear nonlinear models
8.4 Using praccum and forvalues to plot predictions
Options
8.4.1 Example using age and age-squared
8.4.2 Using forvalues with praccum
8.4.3 Using praccum for graphing a transformed variable
8.4.4 Using praccum to graph interactions
8.5 Extending SPost to other estimation commands
8.6 Using Stata more efficiently
8.6.1 profile.do
8.6.2 Changing screen fonts and window preferences
8.6.3 Using ado-files for changing directories
8.6.4 me.hlp file
8.6.5 Scrolling in the Results Window in Windows
8.7 Conclusions

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romae 发表于 2005-4-12 10:36:00 |显示全部楼层
老大 文件格式是什么 怎么打不开 谢谢!

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hanszhu 发表于 2005-4-12 11:06:00 |显示全部楼层

You can unzip by Winrar. Maybe you did not pay the $50 and you got a uncomplete file, and it is also possible that I did not set the 出售帖 properly.

[此贴子已经被作者于2005-4-12 11:10:38编辑过]

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