Stata press college station (43 risultati)

Paperback. Condizione: Very Good. Second Edition; First Printing. 7.25 X 0.5 X 9.25 inches; 333 pages; Property of Economics Department written on the inside of the front cover. Minor shelf wear on the covers. Very Good condition otherwise. No other noteworthy defects. No markings on text pages. ; - Your satisfaction is our priority. We offer free returns and respond promptly to all inquiries. Your item will be packaged with care and ship on the same or next business day. Buy with confidence. …

Soft cover. Condizione: Very Good. 3rd Edition. Rubbed edges and spine ends, creases and rubbing at the corners. The binding is square and firm, text clean.

Paperback. Condizione: new. Paperback. Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata.The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003. Each tip is a brief article that provides practical advice on using Stata. With tips covering a breadth of topics in statistics, graphics, data management, and programming, both new and experienced Stata users are sure to find tips that will be useful in their research. Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata. The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003. Each tip is a brief article that provides practical advice on using Stata. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Alan Acock's book, A Practical Guide to Logistic Regression Using Stata, is written for students and researchers who are new to logistic regression and who want to focus on applications rather than theory. This guide teaches when and why logistic regression is appropriate, how to easily fit these models by using Stata, and how to interpret and present the results.The book begins with a review of OLS regression and an introduction to the concepts of logistic regression. It compares and contrasts these two methods and explains why logistic regression is usually the better approach to modeling binary outcome data. Along the way, readers will learn about parameter estimation for logistic regression models.The author then turns his attention to interpreting the models and assessing model fit. The book demonstrates how to transform the coefficients into more interpretable odds ratios and how to estimate relative risks when appropriate. Acock next explains tools such as the pseudo-R2, likelihood-ratio tests, Akaike's information criterion (AIC), and Schwarz's Bayesian information criterion (BIC) and shows how to use these tools to assess the fit of the model to the data.Subsequent chapters focus on assessing a model's predictive utility using sensitivity, specificity, and receiver operating characteristic (ROC) curves. These concepts are explained clearly and demonstrated with practical examples.The book concludes with a detailed discussion of how to build models with different kinds of predictor variables, how to use Stata's margins command to transform the model coefficients to predicted probabilities, and how to use marginsplot to create easily interpretable visualizations of the results. The author includes many examples using continuous and categorical predictors, illustrates various interactions between different predictor variables, and explains complications that may arise, such as multicollinearity.A Practical Guide to Logistic Regression Using Stata provides a comprehensive, applications-oriented introduction to modeling binary outcomes using logistic regression. Readers at all levels will learn the skills to confidently fit, assess, interpret, and visualize these models using their own data. Alan Acock's book, A Practical Guide to Logistic Regression Using Stata, is for students and researchers who are new to logistic regression and who want to focus on applications, rather than theory. Readers will learn when to use logistic regression in their research and how to fit these models in Stata. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

XIV, 245 Seiten. Gr.-8vo. Kartoniert. Ehemaliges Bibliotheksexemplar, im Impressum gestempelt u. mit Rückenschild. Leichte Gebrauchsspuren. Sprache: Deutsch Gewicht in Gramm: 650.

Paperback. Condizione: Gut. Revised edition. 306 p., with figures, Good condition. Library copy with usual marks. Sprache: Englisch Gewicht in Gramm: 760.

Paperback. Condizione: new. Paperback. An Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. As with previous editions, readers will learn to work effectively in Stata to perform data management, compute descriptive statistics, create meaningful graphs, fit regression models, and perform survival analysis. The fifth edition adds examples of performing power, precision, and sample-size analysis; working with Unicode characters; managing data with ICD-9 and ICD-10 codes; and creating customized tables.With many worked examples and downloadable datasets, this text is the ideal resource for hands-on learning, whether for students in a statistics course or for researchers in fields such as epidemiology, biostatistics, and public health who are learning to use Stata's tools for health research. An Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Maximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Statas commands for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation.The fifth edition includes a new second chapter that demonstrates the easy-to-use mlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming.The core of the book focuses on Stata's ml command. It shows you how to take full advantage of mls noteworthy features:Linear constraintsFour optimization algorithms (NewtonRaphson, DFP, BFGS, and BHHH)Observed information matrix (OIM) variance estimatorOuter product of gradients (OPG) variance estimatorHuber/White/sandwich robust variance estimatorClusterrobust variance estimatorComplete and automatic support for survey data analysisDirect support of evaluator functions written in MataWhen appropriate options are used, many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator.In later chapters, you will learn how to take advantage of Mata, Stata's matrix programming language. For ease of programming and potential speed improvements, you can write your likelihood-evaluator program in Mata and continue to use ml to control the maximization process. A new chapter in the fifth edition shows how you can use the moptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata.In the final chapter, the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit, linear regression, Weibull regression, the Cox proportional hazards model, random-effects regression, and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model, a model that is not available otherwise in Stata.The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book, users will be able to write their own estimation commands---commands that look and behave just like the official estimation commands in Stata.Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use, you need this book. Maximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Learn about ML estimation and how to write Stata code for a special ML estimator for your own research or for a general-purpose ML estimator. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. This second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customized Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode. Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organized by data management areas: reading and writing datasets, cleaning data, labeling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the nuts and bolts examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice. This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time savingthere is a good chance that even the experienced user will learn some new tricks. This second edition focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout. It is useful both as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniquesfrom the incredibly simple to the quite complex and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool. The Revised Edition has been updated for Stata 16. Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniquesfrom the incredibly simple to the quite complex and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. With comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material. The book comprises two volumes. Volume I focuses on linear models for continuous outcomes. Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. Volume I focuses on linear models for continuous outcomes. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: new. Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata.The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003. Each tip is a brief article that provides practical advice on using Stata. With tips covering a breadth of topics in statistics, graphics, data management, and programming, both new and experienced Stata users are sure to find tips that will be useful in their research. Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata. The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. With comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material. The book comprises two volumes. Volume II focuses on generalized linear models for binary, ordinal, count, and other types of outcomes. Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. Volume II focuses on generalized linear models for binary, ordinal, count, and other types of outcomes. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. An Introduction to Survival Analysis Using Stata, Revised Third Edition is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Statas survival analysis routines.The revised third edition has been updated for Stata 14, and it includes a new section on predictive margins and marginal effects, which demonstrates how to obtain and visualize marginal predictions and marginal effects using the margins and marginsplot commands after survival regression models.Survival analysis is a field of its own that requires specialized data management and analysis procedures. To meet this requirement, Stata provides the st family of commands for organizing and summarizing survival data.This book provides statistical theory, step-by-step procedures for analyzing survival data, an in-depth usage guide for Stata's most widely used st commands, and a collection of tips for using Stata to analyze survival data and to present the results. This book develops from first principles the statistical concepts unique to survival data and assumes only a knowledge of basic probability and statistics and a working knowledge of Stata.The first three chapters of the text cover basic theoretical concepts: hazard functions, cumulative hazard functions, and their interpretations; survivor functions; hazard models; and a comparison of nonparametric, semiparametric, and parametric methodologies. Chapter 4 deals with censoring and truncation. The next three chapters cover the formatting, manipulation, stsetting, and error checking involved in preparing survival data for analysis using Stata's st analysis commands. Chapter 8 covers nonparametric methods, including the KaplanMeier and NelsonAalen estimators and the various nonparametric tests for the equality of survival experience.Chapters 911 discuss Cox regression and include various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, model diagnostics, and regression with survey data. The next four chapters cover parametric models, which are fit using Stata's streg command. These chapters include detailed derivations of all six parametric models currently supported in Stata and methods for determining which model is appropriate, as well as information on stratification, obtaining predictions, and advanced topics such as frailty models. Chapter 16 is devoted to power and sample-size calculations for survival studies. The final chapter covers survival analysis in the presence of competing risks. Revison of: Maximum likelihood estimation with Stata / William Gould, Jeffrey Pitblado, William Sribney. c2006. 3rd ed. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Franz Buscha's book, Graphs Everyone Should Know and How to Create Them in Stata, is written for anyone who uses Stata to make graphs. Beginners will find a complete collection of tools for effectively visualizing their data and results. Experienced Stata users are certain to learn some new tricks as well.The chapters of the book are organized into four main sections: graphs for univariate data, graphs for bivariate data, graphs for multivariate data, and special graphs. Each chapter introduces a type of graph, explains when and why it is useful for visualizing a particular kind of data, demonstrates how to create that graph using Stata, and shows a few variations. The special graph section covers topics such as how to create maps, plot equations, create animated graphs, and create other specialty graphs.Readers will find it easy to learn to make graphs by example. Buscha demonstrates most graphs using datasets that are installed with your copy of Stata, so it is straightforward to follow along. He also clearly pairs each graph with the command used to create it in a box just above the graph. If you find a graph that you wish to create with your own data, you can take the command from the box and replace the variable names in the example with your own variable names.Buscha's book has two unique features that distinguish it from other books about Stata graphs. First, the book's goal is to clearly demonstrate how to effectively visualize different kinds of data and results from models using only necessary features. It focuses on important options but does not discuss all the options available for customizing each graph. Second, the book introduces many community-contributed graph commands that are freely available and can be downloaded from the internet. Readers may be unaware of these commands before finding them in this book, and they can learn how to use them quickly rather than spend time trying to write custom code for themselves.Graphs Everyone Should Know and How to Create Them in Stata is a reference you will use again and again as you visualize different types of data. You will quickly find the graphs that are applicable to your data and the Stata commands necessary to create them. Franz Buscha's book, Graphs Everyone Should Know and How to Create Them in Stata, is for anyone who uses Stata to make graphs. Beginners will find a complete collection of tools for effectively visualizing their data and results. Experienced Stata users are certain to learn some new tricks as well. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Through real-world case studies, this book shows how to use Stata to estimate a class of flexible parametric survival models. It discusses the modeling of time-dependent and continuous covariates and looks at how relative survival can be used to measure mortality associated with a particular disease when the cause of death has not been recorded. The book describes simple quantification of differences between any two covariate patterns through calculation of time-dependent hazard ratios, hazard differences, and survival differences. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Interpreting and Visualizing Regression Models Using Stata, Second Edition provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 200 figures, the book illustrates linear models with continuous predictors (modeled linearly, using polynomials, and piecewise), interactions of continuous predictors, categorical predictors, interactions of categorical predictors, and interactions of continuous and categorical predictors. The book also illustrates how to interpret and visualize results from multilevel models, models where time is a continuous predictor, models with time as a categorical predictor, nonlinear models (such as logistic or ordinal logistic regression), and models involving complex survey data. The examples illustrate the use of the margins, marginsplot, contrast, and pwcompare commands. This new edition reflects new and enhanced features added to Stata, most importantly the ability to label statistical output using value labels associated with factor variables. As a result, output regarding marital status is labeled using intuitive labels like Married and Unmarried instead of using numeric values such as 1 and 2. All the statistical output in this new edition capitalizes on this new feature, emphasizing the interpretation of results based on variables labeled using intuitive value labels. Additionally, this second edition illustrates other new features, such as using transparency in graphics to more clearly visualize overlapping confidence intervals and using small sample-size estimation with mixed models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata, this book is for you. This book provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata, this book is for you. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. In this second edition of An Introduction to Stata Programming, the author introduces concepts by providing the background and importance for the topic, presents common uses and examples, then concludes with larger, more applied examples referred to as "cookbook recipes." This is a great reference for anyone who wants to learn Stata programming. For those learning, the author assumes familiarity with Stata and gradually introduces more advanced programming tools. For the more advanced Stata programmer, the book introduces Statas Mata programming language and optimization routines. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Whether you are new to Stata graphics or a seasoned veteran, this book will teach you how to use Stata to make publication-quality graphs that will stand out and enhance your statistical results. With over 1,200 illustrated examples and quick-reference tabs, this book quickly guides you to the information you need for creating and customizing high-quality graphs for any type of statistical data. Each graph is displayed in full color with simple and clear instructions that illustrate how to create and customize graphs using Stata commands. Whether you use this book as a learning tool or a quick reference, you will have the power of Stata graphics at your fingertips. Whether you are new to Stata graphics or a seasoned veteran, this book will teach you how to use Stata to make publication-quality graphs that will stand out and enhance your statistical results. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condizione: new. Paperback. Alan Acock's book, A Practical Guide to Logistic Regression Using Stata, is written for students and researchers who are new to logistic regression and who want to focus on applications rather than theory. This guide teaches when and why logistic regression is appropriate, how to easily fit these models by using Stata, and how to interpret and present the results.The book begins with a review of OLS regression and an introduction to the concepts of logistic regression. It compares and contrasts these two methods and explains why logistic regression is usually the better approach to modeling binary outcome data. Along the way, readers will learn about parameter estimation for logistic regression models.The author then turns his attention to interpreting the models and assessing model fit. The book demonstrates how to transform the coefficients into more interpretable odds ratios and how to estimate relative risks when appropriate. Acock next explains tools such as the pseudo-R2, likelihood-ratio tests, Akaike's information criterion (AIC), and Schwarz's Bayesian information criterion (BIC) and shows how to use these tools to assess the fit of the model to the data.Subsequent chapters focus on assessing a model's predictive utility using sensitivity, specificity, and receiver operating characteristic (ROC) curves. These concepts are explained clearly and demonstrated with practical examples.The book concludes with a detailed discussion of how to build models with different kinds of predictor variables, how to use Stata's margins command to transform the model coefficients to predicted probabilities, and how to use marginsplot to create easily interpretable visualizations of the results. The author includes many examples using continuous and categorical predictors, illustrates various interactions between different predictor variables, and explains complications that may arise, such as multicollinearity.A Practical Guide to Logistic Regression Using Stata provides a comprehensive, applications-oriented introduction to modeling binary outcomes using logistic regression. Readers at all levels will learn the skills to confidently fit, assess, interpret, and visualize these models using their own data. Alan Acock's book, A Practical Guide to Logistic Regression Using Stata, is for students and researchers who are new to logistic regression and who want to focus on applications, rather than theory. Readers will learn when to use logistic regression in their research and how to fit these models in Stata. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, An Introduction to Modern Econometrics Using Stata focuses on the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical examples that show how the theories are applied to real data sets using Stata. As an expert in Stata, the author successfully guides readers from the basic elements of Stata to the core econometric topics. He first describes the fundamental components needed to effectively use Stata. The book then covers the multiple linear regression model, linear and nonlinear Wald tests, constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models. Subsequent chapters center on the consequences of failures of the linear regression model's assumptions. The book also examines indicator variables, interaction effects, weak instruments, underidentification, and generalized method-of-moments estimation. The final chapters introduce panel-data analysis and discrete- and limited-dependent variables and the two appendices discuss how to import data into Stata and Stata programming. Presenting many of the econometric theories used in modern empirical research, this introduction illustrates how to apply these concepts using Stata. The book serves both as a supplementary text for undergraduate and graduate students and as a clear guide for economists and financial analysts. Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, An Introduction to Modern Econometrics Using Stata focuses on the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical examples that show how the theories are applied to real data sets using Stata. As an expert in Stata, the author successfully guides readers from the basic elements of Stata to the core econometric topics. He first describes the fundamental components needed to effectively use Stata. The book then covers the multiple linear regression model, linear and nonlinear Wald tests, constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models. Subsequent chapters center on the consequences of failures of the linear regression model's assumptions. The book also examines indicator variables, interaction effects, weak instruments, underidentification, and generalized method-of-moments estimation. The final chapters introduce panel-data analysis and discrete- and limited-dependent variables and the two appendices discuss how to import data into Stata and Stata programming. Presenting many of the econometric theories used in modern empirical research, this introduction illustrates how to apply these concepts using Stata. The book serves both as a supplementary text for undergraduate and graduate students and as a clear guide for economists and financial analysts. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniquesfrom the incredibly simple to the quite complex and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool. The Revised Edition has been updated for Stata 16. Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniquesfrom the incredibly simple to the quite complex and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. With comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material. The book comprises two volumes. Volume II focuses on generalized linear models for binary, ordinal, count, and other types of outcomes. Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. Volume II focuses on generalized linear models for binary, ordinal, count, and other types of outcomes. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. An Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. As with previous editions, readers will learn to work effectively in Stata to perform data management, compute descriptive statistics, create meaningful graphs, fit regression models, and perform survival analysis. The fifth edition adds examples of performing power, precision, and sample-size analysis; working with Unicode characters; managing data with ICD-9 and ICD-10 codes; and creating customized tables.With many worked examples and downloadable datasets, this text is the ideal resource for hands-on learning, whether for students in a statistics course or for researchers in fields such as epidemiology, biostatistics, and public health who are learning to use Stata's tools for health research. An Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata.The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003. Each tip is a brief article that provides practical advice on using Stata. With tips covering a breadth of topics in statistics, graphics, data management, and programming, both new and experienced Stata users are sure to find tips that will be useful in their research. Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata. The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003. Each tip is a brief article that provides practical advice on using Stata. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. Microeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods.Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata.The second edition is greatly expandedthe new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters onduration modelsrandomized control trials and exogenous treatment effectsendogenous treatment effectsmodels for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects modelsspatial autoregressive modelssemiparametric regressionlasso for prediction and inferenceBayesian analysisAnyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book. Microeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. In this second edition of An Introduction to Stata Programming, the author introduces concepts by providing the background and importance for the topic, presents common uses and examples, then concludes with larger, more applied examples referred to as "cookbook recipes." This is a great reference for anyone who wants to learn Stata programming. For those learning, the author assumes familiarity with Stata and gradually introduces more advanced programming tools. For the more advanced Stata programmer, the book introduces Statas Mata programming language and optimization routines. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. This second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customized Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode. Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organized by data management areas: reading and writing datasets, cleaning data, labeling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the nuts and bolts examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice. This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time savingthere is a good chance that even the experienced user will learn some new tricks. This second edition focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout. It is useful both as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. Alan Acock's book, A Practical Guide to Logistic Regression Using Stata, is written for students and researchers who are new to logistic regression and who want to focus on applications rather than theory. This guide teaches when and why logistic regression is appropriate, how to easily fit these models by using Stata, and how to interpret and present the results.The book begins with a review of OLS regression and an introduction to the concepts of logistic regression. It compares and contrasts these two methods and explains why logistic regression is usually the better approach to modeling binary outcome data. Along the way, readers will learn about parameter estimation for logistic regression models.The author then turns his attention to interpreting the models and assessing model fit. The book demonstrates how to transform the coefficients into more interpretable odds ratios and how to estimate relative risks when appropriate. Acock next explains tools such as the pseudo-R2, likelihood-ratio tests, Akaike's information criterion (AIC), and Schwarz's Bayesian information criterion (BIC) and shows how to use these tools to assess the fit of the model to the data.Subsequent chapters focus on assessing a model's predictive utility using sensitivity, specificity, and receiver operating characteristic (ROC) curves. These concepts are explained clearly and demonstrated with practical examples.The book concludes with a detailed discussion of how to build models with different kinds of predictor variables, how to use Stata's margins command to transform the model coefficients to predicted probabilities, and how to use marginsplot to create easily interpretable visualizations of the results. The author includes many examples using continuous and categorical predictors, illustrates various interactions between different predictor variables, and explains complications that may arise, such as multicollinearity.A Practical Guide to Logistic Regression Using Stata provides a comprehensive, applications-oriented introduction to modeling binary outcomes using logistic regression. Readers at all levels will learn the skills to confidently fit, assess, interpret, and visualize these models using their own data. Alan Acock's book, A Practical Guide to Logistic Regression Using Stata, is for students and researchers who are new to logistic regression and who want to focus on applications, rather than theory. Readers will learn when to use logistic regression in their research and how to fit these models in Stata. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. With comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material. The book comprises two volumes. Volume I focuses on linear models for continuous outcomes. Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. Volume I focuses on linear models for continuous outcomes. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…