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Regression with Graphics

Author : Lawrence C. Hamilton

Publisher : Brooks/Cole

Page : 388 pages

File Size : 12,4 MB

Release : 1992

Category : Mathematics

ISBN :

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Book Description

This text demonstrates how computing power has expanded the role of graphics in analyzing, exploring, and experimenting with raw data. It is primarily intended for students whose research requires more than an introductory statistics course, but who may not have an extensive background in rigorous mathematics. It's also suitable for courses with students of varying mathematical abilities. Hamilton provides students with a practical, realistic, and graphical approach to regression analysis so that they are better prepared to solve real, sometimes messy problems. For students and professors who prefer a heavier mathematical emphasis, the author has included optional sections throughout the text where the formal, mathematical development of the material is explained in greater detail. REGRESSION WITH GRAPHICS is appropriate for use with any (or no) statistical computer package. However, Hamilton used STAT A in the development of the text due to its ease of application and sophisticated graphics capabilities. (STATA is available in a student package from Duxbury including a tutorial by the same author: Hamilton, STATISTICS WITH STAT A, 5.0, 1998; ISBN: 0-534-31874-6.)



An Introduction to Regression Graphics

Author : R. Dennis Cook

Publisher : John Wiley & Sons

Page : 282 pages

File Size : 44,38 MB

Release : 2009-09-25

Category : Mathematics

ISBN : 0470317701

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Book Description

Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.



Applied Regression Including Computing and Graphics

Author : R. Dennis Cook

Publisher : John Wiley & Sons

Page : 632 pages

File Size : 34,6 MB

Release : 2009-09-25

Category : Mathematics

ISBN : 0470317787

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Book Description

A step-by-step guide to computing and graphics in regression analysis In this unique book, leading statisticians Dennis Cook and Sanford Weisberg expertly blend regression fundamentals and cutting-edge graphical techniques. They combine and up- date most of the material from their widely used earlier work, An Introduction to Regression Graphics, and Weisberg's Applied Linear Regression; incorporate the latest in statistical graphics, computing, and regression models; and wind up with a modern, fully integrated approach to one of the most important tools of data analysis. In 23 concise, easy-to-digest chapters, the authors present:? A wealth of simple 2D and 3D graphical techniques, helping visualize results through graphs * An improved version of the user-friendly Arc software, which lets readers promptly implement new ideas * Complete coverage of regression models, including logistic regression and generalized linear models * More than 300 figures, easily reproducible on the computer * Numerous examples and problems based on real data * A companion Web site featuring free software and advice, available at www.wiley.com/mathem atics Accessible, self-contained, and fully referenced, Applied Regression Including Computing and Graphics assumes only a first course in basic statistical methods and provides a bona fide user manual for the Arc software. It is an invaluable resource for anyone interested in learning how to analyze regression problems with confidence and depth.



Regression Graphics

Author : R. Dennis Cook

Publisher : John Wiley & Sons

Page : 378 pages

File Size : 38,53 MB

Release : 2009-09-25

Category : Mathematics

ISBN : 0470317779

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Book Description

An exploration of regression graphics through computer graphics. Recent developments in computer technology have stimulated new and exciting uses for graphics in statistical analyses. Regression Graphics, one of the first graduate-level textbooks on the subject, demonstrates how statisticians, both theoretical and applied, can use these exciting innovations. After developing a relatively new regression context that requires few scope-limiting conditions, Regression Graphics guides readers through the process of analyzing regressions graphically and assessing and selecting models. This innovative reference makes use of a wide range of graphical tools, including 2D and 3D scatterplots, 3D binary response plots, and scatterplot matrices. Supplemented by a companion ftp site, it features numerous data sets and applied examples that are used to elucidate the theory. Other important features of this book include: * Extensive coverage of a relatively new regression context based on dimension-reduction subspaces and sufficient summary plots * Graphical regression, an iterative visualization process for constructing sufficient regression views * Graphics for regressions with a binary response * Graphics for model assessment, including residual plots * Net-effects plots for assessing predictor contributions * Graphics for predictor and response transformations * Inverse regression methods * Access to a Web site of supplemental plots, data sets, and 3D color displays. An ideal text for students in graduate-level courses on statistical analysis, Regression Graphics is also an excellent reference for professional statisticians.



Regression Graphics

Author : R. Dennis Cook

Publisher : John Wiley & Sons

Page : 380 pages

File Size : 17,19 MB

Release : 1998-09-30

Category : Mathematics

ISBN : 9780471193654

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Book Description

Zur graphischen Präsentation von Regressionsdaten gibt es seit dem Vormarsch der Computertechnik vielfältige neue Möglichkeiten, die über die klassischen Ansätze hinausgehen. Der Autor betritt mit seinen Ideen häufig Neuland; er illustriert sie mit zahlreichen Beispielen, Diagrammen und Abbildungen (die entsprechenden 3D- und Farbversionen sind über Internet abrufbar). (11/98)



A Modern Approach to Regression with R

Author : Simon Sheather

Publisher : Springer Science & Business Media

Page : 398 pages

File Size : 12,95 MB

Release : 2009-02-27

Category : Mathematics

ISBN : 0387096086

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Book Description

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.



Image Processing and Jump Regression Analysis

Author : Peihua Qiu

Publisher : John Wiley & Sons

Page : 344 pages

File Size : 44,82 MB

Release : 2005-05-20

Category : Mathematics

ISBN : 0471733164

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Book Description

The first text to bridge the gap between image processing andjump regression analysis Recent statistical tools developed to estimate jump curves andsurfaces have broad applications, specifically in the area of imageprocessing. Often, significant differences in technicalterminologies make communication between the disciplines of imageprocessing and jump regression analysis difficult. Ineasy-to-understand language, Image Processing and JumpRegression Analysis builds a bridge between the worlds ofcomputer graphics and statistics by addressing both the connectionsand the differences between these two disciplines. The authorprovides a systematic analysis of the methodology behindnonparametric jump regression analysis by outlining procedures thatare easy to use, simple to compute, and have proven statisticaltheory behind them. Key topics include: Conventional smoothing procedures Estimation of jump regression curves Estimation of jump location curves of regression surfaces Jump-preserving surface reconstruction based on localsmoothing Edge detection in image processing Edge-preserving image restoration With mathematical proofs kept to a minimum, this book isuniquely accessible to a broad readership. It may be used as aprimary text in nonparametric regression analysis and imageprocessing as well as a reference guide for academicians andindustry professionals focused on image processing or curve/surfaceestimation.



Applied Regression Modeling

Author : Iain Pardoe

Publisher : John Wiley & Sons

Page : 319 pages

File Size : 27,69 MB

Release : 2013-01-07

Category : Mathematics

ISBN : 1118345045

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Book Description

Praise for the First Edition "The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful." —The American Statistician Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications. The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling. In addition, the Second Edition features clarification and expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos. With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.



Handbook of Regression Modeling in People Analytics

Author : Keith McNulty

Publisher : CRC Press

Page : 272 pages

File Size : 21,17 MB

Release : 2021-07-29

Category : Business & Economics

ISBN : 1000427897

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Book Description

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.



Regression Diagnostics

Author : John Fox

Publisher : SAGE Publications

Page : 138 pages

File Size : 50,64 MB

Release : 2019-12-09

Category : Social Science

ISBN : 1544375212

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Book Description

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 (straight-line) relationship between the response and an explanatory variable, is the assumption of linearity warranted? Regression diagnostics not only reveal deficiencies in a regression model that has been fit to data but in many instances may suggest how the model can be improved. The Second Edition of this bestselling volume by John Fox considers two important classes of regression models: the normal linear regression model (LM), in which the response variable is quantitative and assumed to have a normal distribution conditional on the values of the explanatory variables; and generalized linear models (GLMs) in which the conditional distribution of the response variable is a member of an exponential family. R code and data sets for examples within the text can be found on an accompanying website.