Modern Regression Techniques Using R

Modern Regression Techniques Using R

These are all tested using a range of real research examples conducted by the authors in every chapter.

Author: Daniel B Wright

Publisher: SAGE

ISBN: 9781847879028

Category: Mathematics

Page: 204

View: 595

In a way that is refreshingly engaging and readable, Daniel B. Wright and Kamala London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books’ webpage via the SAGE homepage. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested using a range of real research examples conducted by the authors in every chapter.
Categories: Mathematics

Modern Regression Techniques Using R

Modern Regression Techniques Using R

ANOVA as regression ANOVA (ANalysis Of VAriance) is one of the most common
techniques used in psychology. ANOVA is a particular way of conceptualizing a
regression. All ANOVAs are regressions and in this chapter the R regression ...

Author: Daniel B Wright

Publisher: SAGE

ISBN: 9781446244104

Category: Mathematics

Page: 216

View: 552

Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books' webpage: www.sagepub.co.uk/wrightandlondon. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter. Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets. The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.
Categories: Mathematics

Modern Regression Methods

Modern Regression Methods

Praise for the First Edition ". . . provides a very thorough treatment of regression approaches, including techniques not covered in many books . . ." —Technometrics ". . . an excellent book . . . worthwhile for anyone who uses regression ...

Author: Thomas P. Ryan

Publisher: John Wiley & Sons

ISBN: 9780470081860

Category: Mathematics

Page: 642

View: 152

"Over the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one." (The American Statistician) "The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems." (Computing Reviews) Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in 2003. This new edition has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis. The book provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and ridge regression. Unifying key concepts and procedures, this new edition emphasizes applications to provide a more hands-on and comprehensive understanding of regression diagnostics. New features of the Second Edition include: A revised chapter on logistic regression, including improved methods of parameter estimation A new chapter focusing on additional topics of study in regression, including quantile regression, semiparametric regression, and Poisson regression A wealth of new and updated exercises with worked solutions An extensive FTP site complete with Minitab macros, which allow the reader to compute analyses, and specialized procedures Updated references at the end of each chapter that direct the reader to the appropriate resources for further study An accessible guide to state-of-the-art regression techniques, Modern Regression Methods, Second Edition is an excellent book for courses in regression analysis at the upper-undergraduate and graduate levels. It is also a valuable reference for practicing statisticians, engineers, and physical scientists.
Categories: Mathematics

A Modern Approach to Regression with R

A Modern Approach to Regression with R

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models.

Author: Simon Sheather

Publisher: Springer Science & Business Media

ISBN: 9780387096070

Category: Mathematics

Page: 393

View: 211

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. Plots are shown to be an important tool for both building regression models and assessing their validity. We shall see that deciding what to plot and how each plot should be interpreted will be a major challenge. In order to overcome this challenge we shall need to understand the mathematical properties of the fitted regression models and associated diagnostic procedures. As such this will be an area of focus throughout the book. In particular, we shall carefully study the properties of resi- als in order to understand when patterns in residual plots provide direct information about model misspecification and when they do not. The regression output and plots that appear throughout the book have been gen- ated using R. The output from R that appears in this book has been edited in minor ways. On the book web site you will find the R code used in each example in the text.
Categories: Mathematics

R Statistics Cookbook

R Statistics Cookbook

This book will be a useful guide to solving common and not-so-common challenges in statistics.

Author: Francisco Juretig

Publisher: Packt Publishing Ltd

ISBN: 9781789802924

Category: Computers

Page: 448

View: 859

Solve real-world statistical problems using the most popular R packages and techniques Key Features Learn how to apply statistical methods to your everyday research with handy recipes Foster your analytical skills and interpret research across industries and business verticals Perform t-tests, chi-squared tests, and regression analysis using modern statistical techniques Book Description R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools. You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making. By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry. What you will learn Become well versed with recipes that will help you interpret plots with R Formulate advanced statistical models in R to understand its concepts Perform Bayesian regression to predict models and input missing data Use time series analysis for modelling and forecasting temporal data Implement a range of regression techniques for efficient data modelling Get to grips with robust statistics and hidden Markov models Explore ANOVA (Analysis of Variance) and perform hypothesis testing Who this book is for If you are a quantitative researcher, statistician, data analyst, or data scientist looking to tackle various challenges in statistics, this book is what you need! Proficiency in R programming and basic knowledge of linear algebra is necessary to follow along the recipes covered in this book.
Categories: Computers

Multiple Regression in Behavioral Research

Multiple Regression in Behavioral Research

In R . H . Hoyle ( Ed . ) , Structural equation modeling : Concepts , issues , and
applications ( pp . ... Multilevel factor analysis of class and student achievement
components . ... Classical and modern regression with applications ( 2nd ed . ) .

Author: Elazar J. Pedhazur

Publisher: Wadsworth Publishing Company

ISBN: STANFORD:36105210205790

Category: Psychology

Page: 1058

View: 623

This text adopts a data-analysis approach to multiple regression. The author integrates design and analysis, and emphasises learning by example and critiquing published research.
Categories: Psychology

Modern Regression Methods

Modern Regression Methods

Subset Selection in Regression . New York : Chapman and Hall . Montgomery , D
. C . and E . A . Peck ( 1992 ) . Introduction to Linear Regression Analysis , 2nd
edition . New York : Wiley . Myers , R . H . ( 1990 ) . Classical and Modern ...

Author: Thomas P. Ryan

Publisher: Wiley-Interscience

ISBN: UOM:39015050518896

Category: Mathematics

Page: 515

View: 901

The most comprehensive book available on state-of-the-art regression methodology, complete with exercises and solutions This combination book and disk set presents the full range of regression techniques available today to practitioners, researchers, and students of this popular and ever-changing field. Featuring a strong data analysis orientation and a more comprehensive treatment of regression diagnostics than is found in other texts, Modern Regression Methods contains a wealth of material assembled here for the first time, including recently developed techniques and some new methods introduced by the author, as well as fresh approaches to standard concepts. With thorough analyses of real-world data sets and many exercises with worked solutions, this unique resource reinforces learning while providing you with crucial hands-on experience in the practical application of skills. The book offers: In-depth treatment of standard regression methods, including diagnostics, transformations, ridge regression, and variable selection techniques A detailed examination of nonlinear regression, robust regression, and logistic regression, including both exact and maximum likelihood approaches for logistic regression New graphical techniques and transformation strategies for multiple regression and a survey of nonparametric regression Experimental designs for regression Minitab macros to facilitate understanding and use of many of the new methods that are presented Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Modern Regression Methods was among those chosen.
Categories: Mathematics

A Modern Approach to Regression with R

A Modern Approach to Regression with R

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models.

Author: Simon Sheather

Publisher: Springer Science & Business Media

ISBN: 9780387096087

Category: Mathematics

Page: 393

View: 582

This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. Plots are shown to be an important tool for both building regression models and assessing their validity. We shall see that deciding what to plot and how each plot should be interpreted will be a major challenge. In order to overcome this challenge we shall need to understand the mathematical properties of the fitted regression models and associated diagnostic procedures. As such this will be an area of focus throughout the book. In particular, we shall carefully study the properties of resi- als in order to understand when patterns in residual plots provide direct information about model misspecification and when they do not. The regression output and plots that appear throughout the book have been gen- ated using R. The output from R that appears in this book has been edited in minor ways. On the book web site you will find the R code used in each example in the text.
Categories: Mathematics

Chemometrics in Environmental Chemistry Statistical Methods

Chemometrics in Environmental Chemistry   Statistical Methods

References 1 . Draper N , Smith H ( 1981 ) Applied regression analysis . ... Myers
H ( 1990 ) Classical and modern regressions with applications . ... Manne R (
1987 ) Analysis of two partial - least squares algorithms for multivariate
calibration .

Author: Jürgen Einax

Publisher: Springer

ISBN: 3540589414

Category: Science

Page: 334

View: 310

Pattern recognition and other chemometrical techniques are important tools in interpreting environmental data. This volume presents authoritatively state-of-the-art procedures for measuring and handling environmental data. The chapters are written by leading experts.
Categories: Science

Applied Predictive Modeling

Applied Predictive Modeling

While the text is biased against complex equations, a mathematical background is needed for advanced topics. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them.

Author: Max Kuhn

Publisher: Springer

ISBN: 1461468485

Category: Medical

Page: 600

View: 533

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Categories: Medical

Extending the Linear Model with R

Extending the Linear Model with R

This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.

Author: Julian J. Faraway

Publisher: CRC Press

ISBN: 0203492285

Category: Mathematics

Page: 312

View: 601

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the data described in the book is available at http://people.bath.ac.uk/jjf23/ELM/ Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.
Categories: Mathematics

Technometrics

Technometrics

In this list. Jolivet , Emmanuel ( see Huet , Sylvie ) Jöreskog , K. G. ( see Reyment
, Richard ) Jorner , Ulf ( see Wallgren ... Statistical Quality Control Using the SAS
System , 343 Kleinbaum , David G. , Survival Analysis : A Self - Learning Text ,
228 ... Structures and Repeated Measurements , 101 Kutner , M. ( see Neter , J. )
Lahey , J. ( see Elfe , A. ) Launsby , R. ( see ... Thomas P. , Modern Regression
Methods , 426 Saichev , Alexander I. , and Woycznski , Wojbor A. , Distributions
in the ...

Author:

Publisher:

ISBN: UOM:39015047410710

Category: Experimental design

Page:

View: 845

Categories: Experimental design

Proceedings of the Statistical Computing Section

Proceedings of the Statistical Computing Section

Robust Inference Procedures Applied to Regression . Statistical Computing ...
Cook , R . D . and Weisberg , S . ( 1982 ) . Residuals and ... Influence Functions
and Regression Diagnostics , in Modern Data Analysis , eds . Launer , R . , and ...

Author: American Statistical Association. Statistical Computing Section

Publisher:

ISBN: UOM:39015037940429

Category: Mathematical statistics

Page:

View: 229

Papers presented at the annual meeting of the American Statistical Association ...
Categories: Mathematical statistics

Spatial Distribution Variation and Trends in Storm Precipitation Characteristics Associated with Soil Erosion in the United States

Spatial Distribution  Variation  and Trends in Storm Precipitation Characteristics Associated with Soil Erosion in the United States

Such a comparison , with a constant technique , would identify differences in the
R - factor caused by significant changes ... between R - factor values of the
original Wischmeier - Smith study and those from the application of modern
regression ...

Author: Steven E. Hollinger

Publisher:

ISBN: UIUC:30112104641532

Category: Erosion

Page: 90

View: 605

Categories: Erosion

Statistics for the Social Sciences

Statistics for the Social Sciences

STATISTICS FOR THE SOCIAL SCIENCES Rand R. Wilcox University of
Southern California , Los Angeles This book ... of location , modern regression
techniques ; recent developments associated with heteroscedastic procedures ;
the latest ...

Author: Rand R. Wilcox

Publisher:

ISBN: UCSD:31822021361720

Category: Mathematics

Page: 454

View: 311

This book not only provides a simple description of the basic concepts and principles of statistical analysis for the social sciences, it also points to failures of methods and offers ways to correct such problems. For example, it was once thought that standard hypothesis testing procedures for means have relatively high power under nonnormality. Many studies have since demonstrated that power can be very low even with very slight departures from normality. This bookexplains how and why such departures can occur. Statistics for the Social Sciences goes beyond the typical introductory material on probability and statistical inference to incorporate modern topics of critical importance in social science research. The text includes coverage of such topics asrobust methods for comparing measures of location; modern regression techniques; recent developments associated with heteroscedastic procedures; the latest developments in nonparametric statistics; and a full chapter on multiple comparison procedures. The accompanying disk contains more than 150 easy-to-use Minitab macros that provide the student with state-of-the-art statistical methods not available in standard statistical packages. Once the data are read into Minitab, a single command provides access to more powerful, more flexible, and more accurate statistical procedures. Extensive use of real data in examples and exercises Includes a disk with 150 Minitab macros for all the modern, nonstandard procedures Explains why certain methods might fail and what to do to correct such problems Provides exercises at the end of each chapter
Categories: Mathematics

Journal of management

Journal of management

Pp . 113 - 133 in E . F . Borgatta & G . W . Bohrnstedt ( Eds . ) , Sociological
methodology . San Francisco : Jossey ... Pp . 291 - 338 in J . R . Kimberly & R . H .
Miles ( Eds . ) , Organizational life cycles . ... Classical and modern regression
with applications . Boston , MA ... Time series analysis : Regression techniques .
Beverly ...

Author: Southern Management Association

Publisher:

ISBN: 01492063

Category:

Page:

View: 785

Categories:

Early Analyses of LTPP General Pavement Studies Data

Early Analyses of LTPP General Pavement Studies Data

A number of different modeling techniques , each with its own set of strengths
and weaknesses , were suggested by various ... new applications to engineering
systems ) , ( 6 ) Bayesian analysis , ( 7 ) nonlinear regression analysis , ( 8 )
advanced modern regression ... Marshall R . Thompson University of Illinois
Kenneth R .

Author: J. Brent Rauhut

Publisher: Strategic Highway Research Program (Shrp)

ISBN: CORNELL:31924067471965

Category: Pavements

Page: 32

View: 969

This summary presents the results of the first data analyses of the Strategic Highway Research Program Long-Term Pavement Performance (LTPP) project. Data analyzed included information collected up to 1992. These analyses included: 1) developing a data analysis plan, 2) receipt and processing of data into suitable data bases for analysis and conducting statistical evaluations of the data bases, 3) using the LTPP data to evaluate the American Association of State Highway and Transportation Officials (AASHTO) design equations, 4) conducting sensitivity analyses to identify the independent variables that have significant impacts on pavement performance and to quantify the relative impact of each, and 5) using the experience gained from these early data analyses to recommend concepts for future data analyses.
Categories: Pavements

Directory of Graduate Level Statistics Courses

Directory of Graduate Level Statistics Courses

Department : Mathematics Course : M467 Advanced Statistical Techniques Level
: Upper undergraduate - graduate Regularity : It ... But it should be required for all
students majoring in statistics / probability . ... Peck , Introduction to Linear
Regression Analysis , Wiley , 1982 R . Myers , Classical and Modern Regression
s .

Author: Bernhard Flury

Publisher:

ISBN: IND:39000000001540

Category: Statistics

Page: 148

View: 175

Categories: Statistics