Download or read book Regression Basics written by Leo H. Kahane and published by SAGE Publications. This book was released on 2007-11-28 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition • Offers greater coverage of simple panel-data estimation: Because the availability of panel data has increased over the past decade, this new edition includes coverage of estimation with multiple cross-sections of data across time. • Provides an introductory discussion of omitted variables bias: As a problem that frequently arises, this issue is important for those new to regression analysis to understand. • Includes up-to-date advances: Chapter 7 is expanded to include recent developments in regression. • Uses a diverse selection of examples: Engaging examples illustrate the wide application of regression analysis from baseball salaries to presidential voting to British crime rates to U.S. abortion rates and more. • Includes more end-of-chapter problems: This edition offers new questions at the end of chapters that are based on the new examples woven through the book. • Illustrates examples using software programs: Appendix B now includes screenshots to further aid readers working with Microsoft Excel® and SPSS. Intended Audience This is an ideal core or supplemental text for advanced undergraduate and graduate courses such as Regression and Correlation, Sociological Research Methods, Quantitative Research Methods, and Statistical Methods in the fields of economics, public policy, political science, sociology, public affairs, urban planning, education, and geography.
Download or read book Regression Basics written by Leo H. Kahane and published by Taylor & Francis. This book was released on 2024-10-01 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using an accessible, nontechnical approach, the third edition of Regression Basics introduces readers to the fundamentals of statistical regression. Accessible to anyone with an introductory statistics background, the book draws on engaging examples using real-world data and software programs SPSS®, Stata®, and R to illustrate the key concepts of the least squares regression methodology. The book emphasizes the intuition of regression methodology and provides a hands-on approach, as well as helpful end-of-chapter summaries and questions to consolidate learning. This new edition has been substantially revised and enhanced, with features including the following: Fully updated to show procedures in R, SPSS®, and Stata® Color images and substantially revised visual presentation A suite of online resources including data sets, software instructions, and PowerPoint slides for instructors New and updated examples throughout Expanded material to help students overcome "math anxiety" Expanded material on multicollinearity, heteroskedasticity, and robust standard errors This well-paced book is ideal for advanced undergraduate and graduate students focusing on quantitative methods, research design, and statistical regression in the social and behavioral sciences, political science, and economics.
Download or read book Regression Basics written by Leo H. Kahane and published by SAGE Publications. This book was released on 2007-11-28 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition • Offers greater coverage of simple panel-data estimation: Because the availability of panel data has increased over the past decade, this new edition includes coverage of estimation with multiple cross-sections of data across time. • Provides an introductory discussion of omitted variables bias: As a problem that frequently arises, this issue is important for those new to regression analysis to understand. • Includes up-to-date advances: Chapter 7 is expanded to include recent developments in regression. • Uses a diverse selection of examples: Engaging examples illustrate the wide application of regression analysis from baseball salaries to presidential voting to British crime rates to U.S. abortion rates and more. • Includes more end-of-chapter problems: This edition offers new questions at the end of chapters that are based on the new examples woven through the book. • Illustrates examples using software programs: Appendix B now includes screenshots to further aid readers working with Microsoft Excel® and SPSS. Intended Audience This is an ideal core or supplemental text for advanced undergraduate and graduate courses such as Regression and Correlation, Sociological Research Methods, Quantitative Research Methods, and Statistical Methods in the fields of economics, public policy, political science, sociology, public affairs, urban planning, education, and geography.
Download or read book Applied Regression Analysis written by Christer Thrane and published by Routledge. This book was released on 2019-10-16 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data. Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques. This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book’s accessible approach is recommended for students from across the social sciences.
Download or read book Regression Basics written by Leo H Kahane and published by . This book was released on 2024-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Using an accessible, non-technical approach, the third edition of Regression Basics introduces readers to the fundamentals of statistical regression. Accessible to anyone with an introductory statistics background, the book draws on engaging examples using real-world data and software programs SPSSa, Stataa, and R to illustrate the key concepts of the least squares regression methodology. This well-paced book is ideal for advanced undergraduate and graduate students focusing on quantitative methods, research design and statistical regression in the social and behavioural sciences, political science and economics"--
Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Download or read book SAS System for Regression written by Rudolf Freund and published by John Wiley & Sons. This book was released on 2000-12-29 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: SAS® System for Regression Learn to perform a wide variety of regression analyses using SAS® software with this example-driven revised favorite from SAS Publishing. With this Third Edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics covered include performing linear regression analyses using PROC REG diagnosing and providing remedies for data problems, including outliers and multicollinearity. Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. A helpful discussion of theory is supplied where necessary. Some knowledge of both regression and the SAS System are assumed. New for this edition The Third Edition includes revisions, updated material, and new material. You’ll find new information on using SAS/INSIGHT® software regression with a binary response with emphasis on PROC LOGISTIC nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, using the OUTEST option to produce a data set, and using PROC SCORE to predict another data set.
Download or read book Handbook of Regression Modeling in People Analytics written by Keith McNulty and published by CRC Press. This book was released on 2021-07-29 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work.
Download or read book The Basics of Financial Econometrics written by Frank J. Fabozzi and published by John Wiley & Sons. This book was released on 2014-03-04 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible guide to the growing field of financial econometrics As finance and financial products have become more complex, financial econometrics has emerged as a fast-growing field and necessary foundation for anyone involved in quantitative finance. The techniques of financial econometrics facilitate the development and management of new financial instruments by providing models for pricing and risk assessment. In short, financial econometrics is an indispensable component to modern finance. The Basics of Financial Econometrics covers the commonly used techniques in the field without using unnecessary mathematical/statistical analysis. It focuses on foundational ideas and how they are applied. Topics covered include: regression models, factor analysis, volatility estimations, and time series techniques. Covers the basics of financial econometrics—an important topic in quantitative finance Contains several chapters on topics typically not covered even in basic books on econometrics such as model selection, model risk, and mitigating model risk Geared towards both practitioners and finance students who need to understand this dynamic discipline, but may not have advanced mathematical training, this book is a valuable resource on a topic of growing importance.
Download or read book Introduction to Linear Regression Analysis written by Douglas C. Montgomery and published by Wiley-Interscience. This book was released on 2006-07-21 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and up-to-date introduction to the fundamentals of regression analysis The Fourth Edition of Introduction to Linear Regression Analysis describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. This popular book blends both theory and application to equip the reader with an understanding of the basic principles necessary to apply regression model-building techniques in a wide variety of application environments. It assumes a working knowledge of basic statistics and a familiarity with hypothesis testing and confidence intervals, as well as the normal, t, x2, and F distributions. Illustrating all of the major procedures employed by the contemporary software packages MINITAB(r), SAS(r), and S-PLUS(r), the Fourth Edition begins with a general introduction to regression modeling, including typical applications. A host of technical tools are outlined, such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. Subsequent chapters discuss: * Indicator variables and the connection between regression and analysis-of-variance models * Variable selection and model-building techniques and strategies * The multicollinearity problem--its sources, effects, diagnostics, and remedial measures * Robust regression techniques such as M-estimators, and properties of robust estimators * The basics of nonlinear regression * Generalized linear models * Using SAS(r) for regression problems This book is a robust resource that offers solid methodology for statistical practitioners and professionals in the fields of engineering, physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Both the accompanying FTP site, which contains data sets, extensive problem solutions, software hints, and PowerPoint(r) slides, as well as the book's revised presentation of topics in increasing order of complexity, facilitate its use in a classroom setting. With its new exercises and structure, this book is highly recommended for upper-undergraduate and beginning graduate students in mathematics, engineering, and natural sciences. Scientists and engineers will find the book to be an excellent choice for reference and self-study.
Download or read book Classical and Modern Regression with Applications written by Raymond H. Myers and published by Duxbury Resource Center. This book was released on 1990 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression analysis is a vitally important statistical tool, with major advancements made by both practical data analysts and statistical theorists. In CLASSICAL AND MODERN REGRESSION WITH APPLICATIONS, Second Edition, Raymond H. Myers provides a solid foundation in classical regression, while introducing modern techniques. Throughout the text, a broad spectrum of applications are included from the physical sciences, engineering, biology, management, and economics.
Download or read book Regression Analysis written by Jeremy Arkes and published by Routledge. This book was released on 2019-01-21 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rise of "big data," there is an increasing demand to learn the skills needed to undertake sound quantitative analysis without requiring students to spend too much time on high-level math and proofs. This book provides an efficient alternative approach, with more time devoted to the practical aspects of regression analysis and how to recognize the most common pitfalls. By doing so, the book will better prepare readers for conducting, interpreting, and assessing regression analyses, while simultaneously making the material simpler and more enjoyable to learn. Logical and practical in approach, Regression Analysis teaches: (1) the tools for conducting regressions; (2) the concepts needed to design optimal regression models (based on avoiding the pitfalls); and (3) the proper interpretations of regressions. Furthermore, this book emphasizes honesty in research, with a prevalent lesson being that statistical significance is not the goal of research. This book is an ideal introduction to regression analysis for anyone learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand what regressions do, what their limitations are, and what they can tell us. This will be the most engaging book on regression analysis (or Econometrics) you will ever read! A collection of author-created supplementary videos are available at: https://www.youtube.com/channel/UCenm3BWqQyXA2JRKB_QXGyw
Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Download or read book Multiple Regression and Beyond written by Timothy Z. Keith and published by Routledge. This book was released on 2019-01-14 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companion Website materials: https://tzkeith.com/ Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: • Covers both MR and SEM, while explaining their relevance to one another • Includes path analysis, confirmatory factor analysis, and latent growth modeling • Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises • Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: • New chapter on mediation, moderation, and common cause • New chapter on the analysis of interactions with latent variables and multilevel SEM • Expanded coverage of advanced SEM techniques in chapters 18 through 22 • International case studies and examples • Updated instructor and student online resources
Download or read book Regression Analysis and Linear Models written by Richard B. Darlington and published by Guilford Publications. This book was released on 2016-08-22 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable’s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.
Download or read book Basics of Biostatistics written by Jatinder Bali and published by JP Medical Ltd. This book was released on 2016-12-31 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biostatistics is the branch of statistics that deals with data relating to living organisms. This manual is a comprehensive guide to biostatistics for medical students. Beginning with an overview of bioethics in clinical research, an introduction to statistics, and discussion on research methodology, the following sections cover different statistical tests, data interpretation, probability, and other statistical concepts such as demographics and life tables. The final section explains report writing and applying for research grants and a chapter on ‘measurement and error analysis’ focuses on research papers and clinical trials. Key Points Comprehensive guide to biostatistics for medical students Covers research methodology, statistical tests, data interpretation, probability and more Includes other statistical concepts such as demographics and life tables Explains report writing and grant application in depth
Download or read book Linear Regression with Coding Examples in R written by Robert Collins and published by Createspace Independent Publishing Platform. This book was released on 2018-09-13 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an exploration of linear regression technique used for data analysis. The author helps you know how to analyze your data with linear regression algorithm. The various measures used to determine the accuracy of linear regression models have been discussed. The author helps you know how to create a linear regression equation or model mathematically from a dataset. You will also know how to use such to make predictions. The various methods for linear regression have been discussed in detail. The process of implementing linear regression models in R programming language has been discussed. The author also helps you know how to use such models to make predictions. What is Linear Regression? Simple Linear Regression Gradient Descent Linear Regression with R Ordinary Least Squares Regression Stepwise Linear Regression Principal Component Regression Partial Least Squares Regression Tags: linear programming, linear regression r, linear regression book, linear regression models, linear regression analysis, linear regression machine learning, linear regression a mathematical introduction, linear programming project, linear algebra.
