Unavailable
Unavailable
Unavailable
Ebook737 pages16 hours
Applied Regression Analysis
By Norman R. Draper and Harry Smith
Rating: 4 out of 5 stars
4/5
()
Currently unavailable
Currently unavailable
About this ebook
An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.
Unavailable
Related to Applied Regression Analysis
Titles in the series (100)
Measuring Agreement: Models, Methods, and Applications Rating: 0 out of 5 stars0 ratingsMultiple Imputation for Nonresponse in Surveys Rating: 2 out of 5 stars2/5Aspects of Multivariate Statistical Theory Rating: 0 out of 5 stars0 ratingsTime Series Analysis with Long Memory in View Rating: 0 out of 5 stars0 ratingsApplications of Statistics to Industrial Experimentation Rating: 3 out of 5 stars3/5Statistics and Causality: Methods for Applied Empirical Research Rating: 0 out of 5 stars0 ratingsProbability and Conditional Expectation: Fundamentals for the Empirical Sciences Rating: 0 out of 5 stars0 ratingsBusiness Survey Methods Rating: 0 out of 5 stars0 ratingsSequential Stochastic Optimization Rating: 0 out of 5 stars0 ratingsTime Series Analysis: Nonstationary and Noninvertible Distribution Theory Rating: 0 out of 5 stars0 ratingsNonparametric Finance Rating: 0 out of 5 stars0 ratingsLinear Statistical Inference and its Applications Rating: 0 out of 5 stars0 ratingsMeasurement Errors in Surveys Rating: 0 out of 5 stars0 ratingsThe EM Algorithm and Extensions Rating: 0 out of 5 stars0 ratingsRobust Correlation: Theory and Applications Rating: 0 out of 5 stars0 ratingsComputation for the Analysis of Designed Experiments Rating: 0 out of 5 stars0 ratingsTheory of Ridge Regression Estimation with Applications Rating: 0 out of 5 stars0 ratingsRegression With Social Data: Modeling Continuous and Limited Response Variables Rating: 0 out of 5 stars0 ratingsTheory of Probability: A critical introductory treatment Rating: 0 out of 5 stars0 ratingsFundamental Statistical Inference: A Computational Approach Rating: 0 out of 5 stars0 ratingsSensitivity Analysis in Linear Regression Rating: 0 out of 5 stars0 ratingsMethods for Statistical Data Analysis of Multivariate Observations Rating: 0 out of 5 stars0 ratingsFractal-Based Point Processes Rating: 4 out of 5 stars4/5System Reliability Theory: Models and Statistical Methods Rating: 0 out of 5 stars0 ratingsThe Statistical Analysis of Failure Time Data Rating: 0 out of 5 stars0 ratingsA Course in Time Series Analysis Rating: 3 out of 5 stars3/5Nonlinear Statistical Models Rating: 0 out of 5 stars0 ratingsUnivariate Discrete Distributions Rating: 0 out of 5 stars0 ratingsModern Experimental Design Rating: 0 out of 5 stars0 ratingsForecasting with Univariate Box - Jenkins Models: Concepts and Cases Rating: 0 out of 5 stars0 ratings
Mathematics For You
Mental Math Secrets - How To Be a Human Calculator Rating: 5 out of 5 stars5/5Basic Math & Pre-Algebra For Dummies Rating: 4 out of 5 stars4/5Game Theory: A Simple Introduction Rating: 4 out of 5 stars4/5Geometry For Dummies Rating: 5 out of 5 stars5/5Algebra - The Very Basics Rating: 5 out of 5 stars5/5Quantum Physics for Beginners Rating: 4 out of 5 stars4/5Precalculus: A Self-Teaching Guide Rating: 5 out of 5 stars5/5Introducing Game Theory: A Graphic Guide Rating: 4 out of 5 stars4/5The Everything Guide to Algebra: A Step-by-Step Guide to the Basics of Algebra - in Plain English! Rating: 4 out of 5 stars4/5See Ya Later Calculator: Simple Math Tricks You Can Do in Your Head Rating: 4 out of 5 stars4/5Calculus Made Easy Rating: 4 out of 5 stars4/5Is God a Mathematician? Rating: 4 out of 5 stars4/5The Golden Ratio: The Divine Beauty of Mathematics Rating: 5 out of 5 stars5/5My Best Mathematical and Logic Puzzles Rating: 5 out of 5 stars5/5The Little Book of Mathematical Principles, Theories & Things Rating: 3 out of 5 stars3/5Algebra I Workbook For Dummies Rating: 3 out of 5 stars3/5Relativity: The special and the general theory Rating: 5 out of 5 stars5/5The Everything Everyday Math Book: From Tipping to Taxes, All the Real-World, Everyday Math Skills You Need Rating: 5 out of 5 stars5/5The Thirteen Books of the Elements, Vol. 1 Rating: 0 out of 5 stars0 ratingsA Mind for Numbers | Summary Rating: 4 out of 5 stars4/5Algebra II For Dummies Rating: 3 out of 5 stars3/5Real Estate by the Numbers: A Complete Reference Guide to Deal Analysis Rating: 0 out of 5 stars0 ratingsSneaky Math: A Graphic Primer with Projects Rating: 0 out of 5 stars0 ratingsACT Math & Science Prep: Includes 500+ Practice Questions Rating: 3 out of 5 stars3/5
Reviews for Applied Regression Analysis
Rating: 3.8636363636363638 out of 5 stars
4/5
11 ratings1 review
- Rating: 5 out of 5 stars5/5Applied Regression Analysis is, as one might expect, a textbook concerning the methods and application of regression analysis. The book is laid out in the usual fashion: An initial introductory chapter describing the whys and wherefores of linear regression, a second chapter which re-casts the first chapter in terms of matrix algebra, a third chapter on residual analysis and then a series of chapters that branch off into discussing and teaching a variety of regression subjects such as multiple predictor variables, “best” regression equation, model building, multiple regression applied to ANOVA, and a final chapter with an introduction to non-linear regression. For me, as a practicing statistician for many years, what sets this book apart from its counterparts are Chapters 1 and 3. The discussion of the basic concepts of simple linear regression in Chapter 1, particularly the discussion from pages 8 to 31 of the 2nd edition, is simply the best explanation of the process I have encountered. Of particular value are the paragraphs and sentences in section 1.4 – Examining the Regression Equation. I have quoted the words at the bottom of page 22 and the top of page 23 to more people under more circumstances than I can recall. They completely destroy the ridiculous notion offered up in books, papers, internet chat rooms, etc. concerning the supposed need for Y and/or X to be normally distributed before one can use regression analysis to analyze the data. As for Chapter 3 – it clearly explains the NEED for graphical analysis of residuals. It also, by illustration, provides an understanding of why the current general practice of just applying tests such as the Anderson-Darling or the Shapiro-Wilks or any other test for normality of residuals without a first careful examination of the graphs of the residuals guarantees you will go wrong with great assurance. About the only major residual pattern not discussed in Chapter 3 is that of sloped parallel lines. For the interested reader, Searle discussed this pattern in a 1988 article in Technometrics. This copy is the 2nd edition of the book. It has gone into a 3rd edition and is still available. I would recommend this book to anyone interested in learning the methods of linear regression or in obtaining a better understanding of what is going on when you click on “run regression” in whatever statistics package you happen to be using.