Multivariate Reduced-Rank Regression

This textbook for students in the health and social sciences covers the basics of linear model methods with a minimum of mathematics, assuming only a pre-calculus background. Numerous examples drawn from the news and current events with an emphasis on health issues, illustrate the concepts in an immediately accessible way. Methods covered include linear regression models, Poisson regression, logistic regression, proportional hazards regression, survival analysis, and nonparametric regression. The author emphasizes interpretation of computer output in terms of the motivating example. All of the R code is provided and carefully explained, allowing readers to quickly apply the methods to their own data. Plenty of exercises help students think about the issues involved in the analysis and its interpretation. Code and datasets are available for download from the book's website at www.cambridge.org/zelterman

Linear regression is the first step, and often the only step, required to fit a simple model to data. Supported by a Glossary and tutorial appendices, this is an ideal introduction to regression analysis. Includes Matlab code.

A comprehensive and current introduction to the fundamentals of regression analysisIntroduction to Linear Regression Analysis, 6th Edition is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book.The new edition focuses on four key areas of improvement over the fifth edition: New exercises and data setsNew material on generalized regression techniquesThe inclusion of JMP software in key areasCarefully condensing the text where possibleIntroduction to Linear Regression Analysis skillfully blends theory and application in both the conventional and less common uses of regression analysis in today's cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-b

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

Regression diagnostics are methods for determining whether a regression model that has been fit to data adequately represents the structure of the data. For example, if the model assumes a linear (str

Rebecca M. Warner’s bestselling Applied Statistics: From Bivariate Through Multivariate Techniques has been split into two volumes for ease of use over a two-course sequence. This new multivariate sta

1. Added material on important techniques for data mining, including regression trees and neural network models in Chapters 11 and 13.2. The Chapter on logistic regression (Chapter 14) has been extens

The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational

The Critique of Regression presents the most in-depth critique of regression available in the psychoanalytic literature, whilst presenting the first psychoanalytic theory of irreversible lifespan deve

The Critique of Regression presents the most in-depth critique of regression available in the psychoanalytic literature, whilst presenting the first psychoanalytic theory of irreversible lifespan deve

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-s

Contains an overview of several technical topics of Quantile Regression Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-ba

For advanced undergraduate and graduate courses in Social Statistics. An in-depth introduction to today’s most commonly used statistical and multivariate techniques Using Mu

The first book to discuss robust aspects of nonlinear regression—with applications using R softwareRobust Nonlinear Regression: with Applications using R covers a variety of theories and applica

Spatial Regression Models, 2nd ed. illustrates the use of spatial analysis in the social sciences within a regression framework. The book is accessible to readers without prior background in spat

The material covered by this book consists of a collection of regression models beyond linear regression. The linear regression model is presented in the first chapter in order to refresh the reader’s

This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the m

Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line

This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides a

Understanding Regression Analysis: An Introductory Guide presents the fundamentals of regression analysis, from its meaning to uses, in a concise, easy-to-read, and non-technical style. It illustrates

More comprehensive than other texts, this new book covers the classic and cutting edge multivariate techniques used in today’s research. Through clear writing and engaging pedagogy and examples using

More comprehensive than other texts, this new book covers the classic and cutting edge multivariate techniques used in today’s research. Through clear writing and engaging pedagogy and examples using

This booktraces the theory and methodology of multivariate statistical analysis andshows how it can be conducted in practice using the LISREL computer program. Itpresents not only the typical uses of

Conrad Carlberg's Regression Analysis Microsoft® Excel is today's most complete guide to regression analysis with Microsoft Excel for any business analytics or research task. Drawing on his immense ex

This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using clim

Learn the art of regression analysis with PythonAbout This BookBecome competent at implementing regression analysis in PythonSolve some of the complex data science problems related to predicting outco

This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, share

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodolog

Essential Statistics, Regression, and Econometrics, Second Edition, is innovative in its focus on preparing students for regression/econometrics, and in its extended emphasis on statistical reasoning,

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitione

Explaining techniques of multivariate statistical analysis that are widely used in archaeology and archaeological science, Baxter emphasizes the popular methods of principal component analysis, corres

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 tho

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 tho

Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data. The third edition is a complete rew

Newly edited, Israel Rank is the black comedy that inspired the 1946 film "Kind Hearts and Coronets" and the award-winning Broadway show, "A Gentleman's Guide to Love and Murder."Israel Rank has lovin

This book uses applications to introduce multivariate inequalities before describing their explicit forms and theory. It gives many examples of the application of multivariate Bonferroni-type bounds,

This revised and updated edition focuses on constrained ordination (RDA, CCA), variation partitioning and the use of permutation tests of statistical hypotheses about multivariate data. Both classification and modern regression methods (GLM, GAM, loess) are reviewed and species functional traits and spatial structures analysed. Nine case studies of varying difficulty help to illustrate the suggested analytical methods, using the latest version of Canoco 5. All studies utilise descriptive and manipulative approaches, and are supported by data sets and project files available from the book website: http://regent.prf.jcu.cz/maed2/. Written primarily for community ecologists needing to analyse data resulting from field observations and experiments, this book is a valuable resource to students and researchers dealing with both simple and complex ecological problems, such as the variation of biotic communities with environmental conditions or their response to experimental manipulation.

"This book uniquely utilizes visualization tools to explain and study statistical learning methods. Covering classification and regression, the book is divided into two parts. First, various visualiza

This book explores the fun things researchers can do with logistic regression, explicates and simplifies the confounding complexities of understanding what logistic regression is, and provides evidenc