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Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis. Learn more about "The Little Green Book" - QASS Series! Click Here
Book Synopsis Interaction Effects in Multiple Regression by : James Jaccard
Download or read book Interaction Effects in Multiple Regression written by James Jaccard and published by SAGE Publications. This book was released on 2003-03-05 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis. Learn more about "The Little Green Book" - QASS Series! Click Here
Oriented toward the applied researcher with a basic background in multiple regression and logistic regression, this book shows readers the general strategies for testing interactions in logistic regression as well as providing the tools to interpret and understand the meaning of coefficients in equations with product terms. Using completely worked-out examples, the author focuses on the interpretation of the coefficients of interactive logistic models for a wide range of scenarios encountered in the research literature. In addition, the author avoids complex formulas in favor of simple computer-based heuristics that permit the simple calculation of parameter estimates and estimated standard errors that will typically be of interest to applied researchers.
Book Synopsis Interaction Effects in Logistic Regression by : James Jaccard
Download or read book Interaction Effects in Logistic Regression written by James Jaccard and published by SAGE Publications. This book was released on 2001-02-21 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: Oriented toward the applied researcher with a basic background in multiple regression and logistic regression, this book shows readers the general strategies for testing interactions in logistic regression as well as providing the tools to interpret and understand the meaning of coefficients in equations with product terms. Using completely worked-out examples, the author focuses on the interpretation of the coefficients of interactive logistic models for a wide range of scenarios encountered in the research literature. In addition, the author avoids complex formulas in favor of simple computer-based heuristics that permit the simple calculation of parameter estimates and estimated standard errors that will typically be of interest to applied researchers.
This successful book, now available in paperback, provides academics and researchers with a clear set of prescriptions for estimating, testing and probing interactions in regression models. Including the latest research in the area, such as Fuller's work on the corrected/constrained estimator, the book is appropriate for anyone who uses multiple regression to estimate models, or for those enrolled in courses on multivariate statistics.
Book Synopsis Multiple Regression by : Leona S. Aiken
Download or read book Multiple Regression written by Leona S. Aiken and published by SAGE. This book was released on 1991 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This successful book, now available in paperback, provides academics and researchers with a clear set of prescriptions for estimating, testing and probing interactions in regression models. Including the latest research in the area, such as Fuller's work on the corrected/constrained estimator, the book is appropriate for anyone who uses multiple regression to estimate models, or for those enrolled in courses on multivariate statistics.
With detailed examples, this book demonstrates the use of the computer program LISREL and how it can be applied to the analysis of interactions in regression frameworks. The authors consider a wide range of applications including: qualitative moderator variables; longitudinal designs; and product term analysis. They describe different types of measurement error and then present a discussion of latent variable representations of measurement error which serves as the foundation for the analyses described in later chapters. Finally they offer a brief introduction to LISREL and show how it can be used to execute the analyses. Readers can use this book without any prior training in LISREL and will find it an excellent introduction to analytic methods that deal with the problem of measurement error in the analysis of interactions.
Book Synopsis LISREL Approaches to Interaction Effects in Multiple Regression by : James Jaccard
Download or read book LISREL Approaches to Interaction Effects in Multiple Regression written by James Jaccard and published by SAGE. This book was released on 1996-03-21 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: With detailed examples, this book demonstrates the use of the computer program LISREL and how it can be applied to the analysis of interactions in regression frameworks. The authors consider a wide range of applications including: qualitative moderator variables; longitudinal designs; and product term analysis. They describe different types of measurement error and then present a discussion of latent variable representations of measurement error which serves as the foundation for the analyses described in later chapters. Finally they offer a brief introduction to LISREL and show how it can be used to execute the analyses. Readers can use this book without any prior training in LISREL and will find it an excellent introduction to analytic methods that deal with the problem of measurement error in the analysis of interactions.
Book Synopsis Interaction Effects in Multiple Regression by : James Jaccard
Download or read book Interaction Effects in Multiple Regression written by James Jaccard and published by . This book was released on 1991 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Does the stability of personality vary by gender or ethnicity? Does a particular therapy work better to treat clients with one type of personality disorder than those with another? Providing a solution to thorny problems such as these, Aguinis shows readers how to better assess whether the relationship between two variables is moderated by group membership through the use of a statistical technique, moderated multiple regression (MMR). Clearly written, the book requires only basic knowledge of inferential statistics. It helps students, researchers, and practitioners determine whether a particular intervention is likely to yield dissimilar outcomes for members of various groups. Associated computer programs and data sets are available at the author's website (http: //mypage.iu.edu/ haguinis/mmr).
Book Synopsis Regression Analysis for Categorical Moderators by : Herman Aguinis
Download or read book Regression Analysis for Categorical Moderators written by Herman Aguinis and published by Guilford Press. This book was released on 2004-01-01 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Does the stability of personality vary by gender or ethnicity? Does a particular therapy work better to treat clients with one type of personality disorder than those with another? Providing a solution to thorny problems such as these, Aguinis shows readers how to better assess whether the relationship between two variables is moderated by group membership through the use of a statistical technique, moderated multiple regression (MMR). Clearly written, the book requires only basic knowledge of inferential statistics. It helps students, researchers, and practitioners determine whether a particular intervention is likely to yield dissimilar outcomes for members of various groups. Associated computer programs and data sets are available at the author's website (http: //mypage.iu.edu/ haguinis/mmr).
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
Book Synopsis Statistical Inference via Data Science: A ModernDive into R and the Tidyverse by : Chester Ismay
Download or read book Statistical Inference via Data Science: A ModernDive into R and the Tidyverse written by Chester Ismay and published by CRC Press. This book was released on 2019-12-23 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
Book Synopsis Beyond Multiple Linear Regression by : Paul Roback
Download or read book Beyond Multiple Linear Regression written by Paul Roback and published by CRC Press. This book was released on 2021-01-14 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
It is often necessary for social scientists to study differences in groups, such as gender or race differences in attitudes, buying behavior, or socioeconomic characteristics. When the researcher seeks to estimate group differences through the use of independent variables that are qualitative, dummy variables allow the researcher to represent information about group membership in quantitative terms without imposing unrealistic measurement assumptions on the categorical variables. Beginning with the simplest model, Hardy probes the use of dummy variable regression in increasingly complex specifications, exploring issues such as: interaction, heteroscedasticity, multiple comparisons and significance testing, the use of effects or contrast coding, testing for curvilinearity, and estimating a piecewise linear regression.
Book Synopsis Regression with Dummy Variables by : Melissa A. Hardy
Download or read book Regression with Dummy Variables written by Melissa A. Hardy and published by SAGE. This book was released on 1993-02-25 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is often necessary for social scientists to study differences in groups, such as gender or race differences in attitudes, buying behavior, or socioeconomic characteristics. When the researcher seeks to estimate group differences through the use of independent variables that are qualitative, dummy variables allow the researcher to represent information about group membership in quantitative terms without imposing unrealistic measurement assumptions on the categorical variables. Beginning with the simplest model, Hardy probes the use of dummy variable regression in increasingly complex specifications, exploring issues such as: interaction, heteroscedasticity, multiple comparisons and significance testing, the use of effects or contrast coding, testing for curvilinearity, and estimating a piecewise linear regression.
