Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped-whether those groups are formed by a nesting structure, such as children nested in classrooms, or formed by repeated observations on the same individuals. This text introduces random-effects models, fixed-effects models, mixed-effects models, marginal models, dynamic models, and growth-curve models, all of which account for the grouped nature of these types of data. As Rabe-Hesketh and Skrondal introduce each model, they explain when the model is useful, its assumptions, how to fit and evaluate the model using Stata, and how to interpret the results. With this 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, while volume II focuses on generalized linear models for binary, ordinal, count, and other types of outcomes.

Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:* classification using standard machinery (naive bayes; nearest neighbor; SVM)* clustering and vector quantization (largely as in PSCS)* PCA (largely as in PSCS)* variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)* linear regression (largely as in PSCS)* generalized linear models including logistic regression* model selection with Lasso, elasticnet* robustness and m-estimators* Markov chains and HMM's (largely as in PSCS)* EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they've been through that, the next one is easy* simple graphical models (in the variational inference section)* classification with neural networks, with a particular emphasis onimage classification* autoencoding with neural networks* structure learning

Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you're doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Given this book's accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.

This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo's modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

SAS programming is a creative and iterative process designed to empower you to make the most of your organization's data. This friendly guide provides you with a repertoire of essential SAS tools for data management, whether you are a new or an infrequent user. Most useful to students and programmers with little or no SAS experience, it takes a no-frills, hands-on tutorial approach to getting started with the software. You will find immediate guidance in navigating, exploring, visualizing, cleaning, formatting, and reporting on data using SAS and JMP. Step-by-step demonstrations, screenshots, handy tips, and practical exercises with solutions equip you to explore, interpret, process and summarize data independently, efficiently and effectively.

This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.

The Workflow of Data Analysis Using Stata, by J. Scott Long, is an essential productivity tool for data analysts. Long presents lessons gained from his experience and demonstrates how to design and implement efficient workflows for both one-person projects and team projects. After introducing workflows and explaining how a better workflow can make it easier to work with data, Long describes planning, organizing, and documenting your work. He then introduces how to write and debug Stata do-files and how to use local and global macros. After a discussion of conventions that greatly simplify data analysis the author covers cleaning, analyzing, and protecting data.

Ideal for researchers and graduate students in the social sciences who require knowledge of structural equation modeling techniques to answer substantive research questions, Using Mplus for Structural Equation Modeling provides a reader-friendly introduction to the major types of structural equation models implemented in the Mplus framework. This practical book, which updates author E. Kevin Kelloway's 1998 book Using LISREL for Structural Equation Modeling, retains the successful five-step process employed in the earlier book, with a thorough update for use in the Mplus environment. Kelloway provides an overview of structural equation modeling techniques in Mplus, including the estimation of confirmatory factor analysis and observed variable path analysis. He also covers multilevel modeling for hypothesis testing in real life settings and offers an introduction to the extended capabilities of Mplus, such as exploratory structural equation modeling and estimation and testing of mediated relationships. A sample application with the source code, printout, and results is presented for each type of analysis.

The Survivor's Guide to R provides a gentle, but thorough, introduction to R. It is an ideal supplement to any introductory statistics text or a practical field guide for those who want to use the powerful R language for statistical analysis in their own research. The book focuses on providing students with the real-world R skills that are often hard to get to in statistics classes: basic data management and manipulation, and working with R graphics. The book is designed to get students with little or no background in statistics or programming started on R within the context of a statistics class, and to ensure that they have acquired functional R skills that they can continue to use as they move on to their own projects. The book begins with a straightforward approach to understanding R objects, and then moves systematically through the use of R to transform, sort, and aggregate data; to work with complex textual and date/time data; and to effectively build on R's default graphics capabilities to produce highly customized and effective graphics. It focuses on working with real-world data, with - on reading data in different formats and the challenges of missing data. This book is intended for those with little to no statistics or programming experience---students and other new users who are likely to find their first encounter with R more than a little intimidating. It is written in an accessible and sympathetic style that makes minimal assumptions about user skills, and provides frequent warnings about common pitfalls that must be avoided along the road to R mastery.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. A practical, professional guide to MATLABcomputational techniques and engineering applicationsMATLAB Numerical Methods with Chemical Engineering Applications shows you, step by step, how to use MATLAB (R) to model and simulate physical problems in the chemical engineering realm. Written for MATLAB 7.11, this hands-on resource contains concise explanations of essential MATLAB commands, as well as easy-to-follow instructions for using the programming features, graphical capabilities, and desktop interface. Every step needed toward the final solution is algorithmically explained via snapshots of the MATLAB platform in parallel with the text. End-of-chapterproblems help you practice what you've learned. Master this powerful computational tool using this detailed, self-teaching guide. COVERAGE INCLUDES: MATLAB basics Matrices MATLAB scripting language: M-file Image and image analysis Curve-fitting Numerical integration Solving differential equations A system of algebraic equations Statistics Chemical engineering applications MATLAB Graphical User Interface Design Environment (GUIDE)

Basic Statistics provides an accessible and comprehensive introduction to statistics using the free, state-of-the-art, powerful software program R. This book is designed to both introduce students to key concepts in statistics and to provide simple instructions for using R. This concise book: .Teaches essential concepts in statistics, assuming little background knowledge on the part of the reader .Introduces students to R with as few sub-commands as possible for ease of use .Provides practical examples from the educational, behavioral, and social sciences With clear explanations of statistical processes and step-by-step commands in R, Basic Statistics will appeal to students and professionals across the social and behavioral sciences.

Bridging an understanding of Statistics and SPSS. "The text is written in a user-friendly language and illustrates concepts that would otherwise be confusing to beginning students and those with limited computer skills." -Justice Mbizo, University of West Florida This unique text helps students develop a conceptual understanding of a variety of statistical tests by linking the ideas learned in a statistics class from a traditional statistics textbook with the computational steps and output from SPSS. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, clearly linking how the SPSS procedure and output connect back to the conceptual underpinnings of the test. By drawing clear connections between the theoretical and computational aspects of statistics, this engaging text aids students' understanding of theoretical concepts by teaching them in a practical context.

This is the first book on parallel MATLAB and the first parallel computing book focused on the design, code, debug, and test techniques required to quickly produce well-performing parallel programs. MATLAB is currently the dominant language of technical computing with one million users worldwide, many of whom can benefit from the increased power offered by inexpensive multicore and multinode parallel computers. MATLAB is an ideal environment for learning about parallel computing, allowing the user to focus on parallel algorithms instead of the details of implementation. Parallel MATLAB for Multicore and Multinode Computers covers more parallel algorithms and parallel programming models than any other parallel programming book due to the succinctness of MATLAB. It presents a hands-on approach with numerous example programs; wherever possible, the examples are drawn from widely known and well-documented parallel benchmark codes that are representative of many real applications across the field of technical computing.

Diese Arbeit zeigt einen f]r die Praxis gangbaren Weg zur Umsetzung zeitbezogener Daten in betrieblichen Informationssystemen auf. Das vorgestellte Modell bietet daf]r einen strukturierten Ansatz, mit dem es mvglich ist ohne Modifikation des DBMS-Kerns und ohne eine Zwischenschicht (z.B.: Pre-Compiler), den temporalen Aspekt in gdngigen kommerziellen DBMS angemessen zu ber]cksichtigen. Das vorgestellte Modell erweist sich flexibel genug um auch analog auf den objektrelationalen Bereich angewendet werden zu kvnnen.

Do you need to conduct data analysis with SPSS but are unfamiliar with the software? This user-friendly book-a SAGE bestseller-helps you become proficient in SPSS by teaching you the fundamentals of SPSS. An Introductory Guide to SPSS (R) for Windows (R), Second Edition develops SPSS skills using sample programs illustrating how to conduct the analyses typically covered in an introductory statistics course. Throughout the book, data are analyzed and SPSS output are interpreted in the context of research questions. Boldface text is used to indicate operations or choices the reader will need to make when running SPSS. Exercises are also included, with solutions provided in the appendix. "I want to commend the author for including a chapter on using the SPSS manuals. I would not have thought of this addition, and the information helps the reader go on to learn the more complex components of SPSS." -Laura Myers, University of Georgia This Second Edition has been updated to SPSS Version 12.0, although its approach makes it useful for readers running other versions. Each chapter in this updated text includes a statement of its purpose and goal, as well as a chapter glossary. The updated text includes new SPSS features, including how to recode data using the Visual Bander and how to read text data using the Text Import Wizard. Author Eric L. Einspruch thoroughly covers critical basic skills: * How to create data sets by defining and coding data, using a codebook, and entering data * How to run SPSS and work with different SPSS files * How to manipulate data by recoding values, computing values, and selecting subsets of cases to include in an analysis * How to manage data files by reading data that have been entered using other software, appending files, and merging files * How to analyze data using SPSS pull-down menus * How to analyze data using programs written in SPSS syntax This outstanding book concludes with a discussion of how to get help in SPSS, suggestions on how to make the most of SPSS manuals, and directions for taking the next steps toward software mastery.

An Intermediate Guide to SPSS Programming: Using Syntax for Data Management introduces the major tasks of data management and presents solutions using SPSS syntax. This book fills an important gap in the education of many students and researchers, whose coursework has left them unprepared for the data management issues that confront them when they begin to do independent research. It also serves as an introduction to SPSS programming. All the basic features of SPSS syntax are illustrated, as are many intermediate and advanced topics such as using vectors and loops, reading complex data files, and using the SPSS macro language. An Intermediate Guide to SPSS Programming will be a welcome addition to advanced undergraduate and graduate statistics courses across the social sciences, education, and health. Professional researchers, data managers, and statisticians will also find this an invaluable reference for SPSS and data management.

Second Edition

Discovering Structural Equation Modeling Using Stata, Revised Edition is devoted to Stata's sem command and all it can do. Learn about its capabilities in the context of confirmatory factor analysis, path analysis, structural equation modeling, longitudinal models, and multiple-group analysis. Each model is presented along with the necessary Stata code, which is parsimonious, powerful, and can be modified to fit a wide variety of models. The datasets used are downloadable, offering a hands-on approach to learning. A particularly exciting feature of Stata is the SEM Builder. This graphical interface for structural equation modeling allows you to draw publication-quality path diagrams and fit the models without writing any programming code. When you fit a model with the SEM Builder, Stata automatically generates the complete code that you can save for future use. Use of this unique tool is extensively covered in an appendix and brief examples appear throughout the text.

A friendly, straightforward guide that does not assume knowledge of programming, this book helps new R users hit the ground running. Eric L. Einspruch provides an overview of the software and shows how to download and install R, RStudio, and R packages. Featuring example code, screenshots, tips, learning exercises, and worked-through examples of statistical techniques, the book demonstrates the capabilities and nuances of these powerful free statistical analysis and data visualization tools. Fundamental aspects of data wrangling, analysis, visualization, and reporting are introduced, using both Base R and Tidyverse approaches. Einspruch emphasizes processes that support research reproducibility, such as use of comments to document R code and use of R Markdown capabilities. The book also helps readers navigate the vast array of R resources available to further develop their skills.

Aspects of environmental change are some of the greatest challenges faced by policymakers today. The key issues addressed by environmental science are often empirical, and in many instances very detailed, sizable datasets are available. Researchers in this field should have a solid understanding of the econometric tools best suited for analysis of these data. While complex and expensive physical models of the environment exist, it is becoming increasingly clear that reduced-form econometric models have an important role to play in modeling environmental phenomena. In short, successful environmental modeling does not necessarily require a structural model, but the econometric methods underlying a reduced-form approach must be competently executed. Environmental Econometrics Using Stata provides an important starting point for this journey by presenting a broad range of applied econometric techniques for environmental econometrics and illustrating how they can be applied in Stata. The emphasis is not only on how to formulate and fit models in Stata but also on the need to use a wide range of diagnostic tests in order to validate the results of estimation and subsequent policy conclusions. This focus on careful, reproducible research should be appreciated by academic and non-academic researchers who are seeking to produce credible, defensible conclusions about key issues in environmental science.

"This would be an excellent book for undergraduate, graduate and beyond....The style of writing is easy to read and the author does a good job of adding humor in places. The integration of basic programming in R with the data that is collected for any experiment provides a powerful platform for analysis of data.... having the understanding of data analysis that this book offers will really help researchers examine their data and consider its value from multiple perspectives - and this applies to people who have small AND large data sets alike! This book also helps people use a free and basic software system for processing and plotting simple to complex functions." Michelle Pantoya, Texas Tech University Measurements of quantities that vary in a continuous fashion, e.g., the pressure of a gas, cannot be measured exactly and there will always be some uncertainty with these measured values, so it is vital for researchers to be able to quantify this data. Uncertainty Analysis of Experimental Data with R covers methods for evaluation of uncertainties in experimental data, as well as predictions made using these data, with implementation in R. The books discusses both basic and more complex methods including linear regression, nonlinear regression, and kernel smoothing curve fits, as well as Taylor Series, Monte Carlo and Bayesian approaches. Features: 1. Extensive use of modern open source software (R). 2. Many code examples are provided. 3. The uncertainty analyses conform to accepted professional standards (ASME). 4. The book is self-contained and includes all necessary material including chapters on statistics and programming in R. Benjamin D. Shaw is a professor in the Mechanical and Aerospace Engineering Department at the University of California, Davis. His research interests are primarily in experimental and theoretical aspects of combustion. Along with other courses, he has taught undergraduate and graduate courses on engineering experimentation and uncertainty analysis. He has published widely in archival journals and became an ASME Fellow in 2003.

This book focuses is on data science. It includes plenty of actual examples of the typical data processing and data presentations required of a professional data scientist. The material will be especially useful to the growing profession of data scientists. As a practitioner, the author brings a practical view on the topic, with a very hands-on oriented presentation that will be particularly useful to other practitioners. The book also concentrates on the current generation of R packages that have added considerable capability to R, including Hadley Wickam's suite of packages, such as tidyr, dplyr, lubridate, stringr, and ggplot2.