Applied Regression Analysis And Experimental Design
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Applied Regression Analysis And Experimental Design
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Author : Richard J. Brook
language : en
Publisher: CRC Press
Release Date : 2018-12-13
Applied Regression Analysis And Experimental Design written by Richard J. Brook and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-13 with Mathematics categories.
For a solid foundation of important statistical methods, the concise, single-source text unites linear regression with analysis of experiments and provides students with the practical understanding needed to apply theory in real data analysis problems. Stressing principles while keeping computational and theoretical details at a manageable level, Applied Regression Analysis and Experimental Design features an emphasis on vector geometry and least squares to unify and provide an intuitive basis for most topics covered... abundant examples and exercises using real-life data sets clearly illustrating practical of data analysis...essential exposure to MINITAB and GENSTAT computer packages , including computer printouts...and important background material such as vector and matrix properties and the distributional properties of quadratic forms. Designed to make theory work for students, this clearly written, easy-to-understand work serves as the ideal texts for courses Regression, Experimental Design, and Linear Models in a broad range of disciplines. Moreover, applied statisticians will find the book a useful reference for the general application of the linear model.
Applied Regression Analysis
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Author : John O. Rawlings
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-06
Applied Regression Analysis written by John O. Rawlings and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-06 with Mathematics categories.
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to statistical methods and a thoeretical linear models course. Applied Regression Analysis emphasizes the concepts and the analysis of data sets. It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models are also discussed. Detailed case studies and exercises based on real data sets are used to reinforce the concepts. The data sets used in the book are available on the Internet.
Applied Linear Statistical Models
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Author : John Neter
language : en
Publisher: Irwin Professional Publishing
Release Date : 1974
Applied Linear Statistical Models written by John Neter and has been published by Irwin Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 1974 with Mathematics categories.
Some basic results in probability and statistics. Basic regression analysis. General regression and correlation analysis. Basic analysis of variance. Multifactor analysis of variance. Experimental designs.
Applied Multivariate Data Analysis
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Author : J.D. Jobson
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Applied Multivariate Data Analysis written by J.D. Jobson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.
Applied Regression Analysis
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Author :
language : en
Publisher:
Release Date : 2013
Applied Regression Analysis written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Analysis of variance categories.
Analysis Of Variance Design And Regression
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Author : Ronald Christensen
language : en
Publisher: CRC Press
Release Date : 1996-06-01
Analysis Of Variance Design And Regression written by Ronald Christensen and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-06-01 with Mathematics categories.
This text presents a comprehensive treatment of basic statistical methods and their applications. It focuses on the analysis of variance and regression, but also addressing basic ideas in experimental design and count data. The book has four connecting themes: similarity of inferential procedures, balanced one-way analysis of variance, comparison of models, and checking assumptions. Most inferential procedures are based on identifying a scalar parameter of interest, estimating that parameter, obtaining the standard error of the estimate, and identifying the appropriate reference distribution. Given these items, the inferential procedures are identical for various parameters. Balanced one-way analysis of variance has a simple, intuitive interpretation in terms of comparing the sample variance of the group means with the mean of the sample variance for each group. All balanced analysis of variance problems are considered in terms of computing sample variances for various group means. Comparing different models provides a structure for examining both balanced and unbalanced analysis of variance problems and regression problems. Checking assumptions is presented as a crucial part of every statistical analysis. Examples using real data from a wide variety of fields are used to motivate theory. Christensen consistently examines residual plots and presents alternative analyses using different transformation and case deletions. Detailed examination of interactions, three factor analysis of variance, and a split-plot design with four factors are included. The numerous exercises emphasize analysis of real data. Senior undergraduate and graduate students in statistics and graduate students in other disciplines using analysis of variance, design of experiments, or regression analysis will find this book useful.
Applied Regression
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Author : Colin Lewis-Beck
language : en
Publisher: SAGE Publications
Release Date : 2015-06-18
Applied Regression written by Colin Lewis-Beck and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-18 with Social Science categories.
Known for its readability and clarity, this Second Edition of the best-selling Applied Regression provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. The authors then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.
Applied Regression And Anova Using Sas
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Author : Patricia F. Moodie
language : en
Publisher: CRC Press
Release Date : 2022-06-07
Applied Regression And Anova Using Sas written by Patricia F. Moodie and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-07 with Mathematics categories.
Applied Regression and ANOVA Using SAS® has been written specifically for non-statisticians and applied statisticians who are primarily interested in what their data are revealing. Interpretation of results are key throughout this intermediate-level applied statistics book. The authors introduce each method by discussing its characteristic features, reasons for its use, and its underlying assumptions. They then guide readers in applying each method by suggesting a step-by-step approach while providing annotated SAS programs to implement these steps. Those unfamiliar with SAS software will find this book helpful as SAS programming basics are covered in the first chapter. Subsequent chapters give programming details on a need-to-know basis. Experienced as well as entry-level SAS users will find the book useful in applying linear regression and ANOVA methods, as explanations of SAS statements and options chosen for specific methods are provided. Features: •Statistical concepts presented in words without matrix algebra and calculus •Numerous SAS programs, including examples which require minimum programming effort to produce high resolution publication-ready graphics •Practical advice on interpreting results in light of relatively recent views on threshold p-values, multiple testing, simultaneous confidence intervals, confounding adjustment, bootstrapping, and predictor variable selection •Suggestions of alternative approaches when a method’s ideal inference conditions are unreasonable for one’s data This book is invaluable for non-statisticians and applied statisticians who analyze and interpret real-world data. It could be used in a graduate level course for non-statistical disciplines as well as in an applied undergraduate course in statistics or biostatistics.
Applied Linear Statistical Models Regression Analysis Of Variance And Experimental Design
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Author : John Neter
language : en
Publisher:
Release Date : 1977
Applied Linear Statistical Models Regression Analysis Of Variance And Experimental Design written by John Neter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1977 with Analisis de regresion categories.
Applied Regression Analysis And Generalized Linear Models
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Author : John Fox
language : en
Publisher: SAGE Publications
Release Date : 2015-03-18
Applied Regression Analysis And Generalized Linear Models written by John Fox and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-18 with Social Science categories.
Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website.