Download or read book Analysis of Variance Design and Regression written by Ronald Christensen and published by CRC Press. This book was released on 1996-06-01 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Analysis of Variance Design and Regression written by Ronald Christensen and published by CRC Press. This book was released on 2018-09-03 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data. New to the Second Edition Reorganized to focus on unbalanced data Reworked balanced analyses using methods for unbalanced data Introductions to nonparametric and lasso regression Introductions to general additive and generalized additive models Examination of homologous factors Unbalanced split plot analyses Extensions to generalized linear models R, Minitab®, and SAS code on the author’s website The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.
Download or read book Data Analysis for Research Designs written by Geoffrey Keppel and published by Macmillan. This book was released on 1989-03-15 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analysis for Research Designs covers the analytical techniques for the analysis of variance (ANOVA) and multiple regression/correlation (MRC), emphasizing single-degree-of-freedom comparisons so that students focus on clear research planning. This text is designed for advanced undergraduates and graduate students of the behavioral and social sciences who have an understanding of algebra and statistics.
Download or read book Introduction to Mixed Modelling written by N. W. Galwey and published by John Wiley & Sons. This book was released on 2007-04-04 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mixed modelling is one of the most promising and exciting areas ofstatistical analysis, enabling more powerful interpretation of datathrough the recognition of random effects. However, many perceivemixed modelling as an intimidating and specialized technique. Thisbook introduces mixed modelling analysis in a simple andstraightforward way, allowing the reader to apply the techniqueconfidently in a wide range of situations. Introduction to Mixed Modelling shows that mixedmodelling is a natural extension of the more familiar statisticalmethods of regression analysis and analysis of variance. In doingso, it provides the ideal introduction to this importantstatistical technique for those engaged in the statistical analysisof data. This essential book: Demonstrates the power of mixed modelling in a wide range ofdisciplines, including industrial research, social sciences,genetics, clinical research, ecology and agriculturalresearch. Illustrates how the capabilities of regression analysis can becombined with those of ANOVA by the specification of a mixedmodel. Introduces the criterion of Restricted Maximum Likelihood(REML) for the fitting of a mixed model to data. Presents the application of mixed model analysis to a widerange of situations and explains how to obtain and interpret BestLinear Unbiased Predictors (BLUPs). Features a supplementary website containing solutions toexercises, further examples, and links to the computer softwaresystems GenStat and R. This book provides a comprehensive introduction to mixedmodelling, ideal for final year undergraduate students,postgraduate students and professional researchers alike. Readerswill come from a wide range of scientific disciplines includingstatistics, biology, bioinformatics, medicine, agriculture,engineering, economics, and social sciences.
Download or read book Analysis of Variance Designs written by Glenn Gamst and published by . This book was released on 2008 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook explains ANOVA designs for advanced undergraduates and graduate students in the behavioural sciences.
Download or read book Applied Linear Statistical Models written by John Neter and published by Irwin Professional Publishing. This book was released on 1974 with total page 870 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book ANOVA and ANCOVA written by Andrew Rutherford and published by John Wiley & Sons. This book was released on 2012-08-29 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an in-depth treatment of ANOVA and ANCOVA techniques from a linear model perspective ANOVA and ANCOVA: A GLM Approach provides a contemporary look at the general linear model (GLM) approach to the analysis of variance (ANOVA) of one- and two-factor psychological experiments. With its organized and comprehensive presentation, the book successfully guides readers through conventional statistical concepts and how to interpret them in GLM terms, treating the main single- and multi-factor designs as they relate to ANOVA and ANCOVA. The book begins with a brief history of the separate development of ANOVA and regression analyses, and then goes on to demonstrate how both analyses are incorporated into the understanding of GLMs. This new edition now explains specific and multiple comparisons of experimental conditions before and after the Omnibus ANOVA, and describes the estimation of effect sizes and power analyses leading to the determination of appropriate sample sizes for experiments to be conducted. Topics that have been expanded upon and added include: Discussion of optimal experimental designs Different approaches to carrying out the simple effect analyses and pairwise comparisons with a focus on related and repeated measure analyses The issue of inflated Type 1 error due to multiple hypotheses testing Worked examples of Shaffer's R test, which accommodates logical relations amongst hypotheses ANOVA and ANCOVA: A GLM Approach, Second Edition is an excellent book for courses on linear modeling at the graduate level. It is also a suitable reference for researchers and practitioners in the fields of psychology and the biomedical and social sciences.
Download or read book Applied Statistics written by Ruth M. Mickey and published by Wiley-Blackwell. This book was released on 2004-02-06 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition of this standard has been extensively revised to serve the ever-increasing number of investigators who, with only a year or so od statistical training, are performing sophisticated statistical analyses on computers.
Download or read book Experimental Design and the Analysis of Variance written by Robert K. Leik and published by SAGE Publications. This book was released on 1997-04-19 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why is this Book a Useful Supplement for Your Statistics Course? Most core statistics texts cover subjects like analysis of variance and regression, but not in much detail. This book, as part of our Series in Research Methods and Statistics, provides you with the flexibility to cover ANOVA more thoroughly, but without financially overburdening your students.
Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Download or read book Applied Regression Analysis and Experimental Design written by Richard J. Brook and published by Routledge. This book was released on 2018-12-13 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Methods and Applications of Linear Models written by Ronald R. Hocking and published by John Wiley & Sons. This book was released on 2005-02-04 with total page 773 pages. Available in PDF, EPUB and Kindle. Book excerpt: A popular statistical text now updated and better than ever! The ready availability of high-speed computers and statistical software encourages the analysis of ever larger and more complex problems while at the same time increasing the likelihood of improper usage. That is why it is increasingly important to educate end users in the correct interpretation of the methodologies involved. Now in its second edition, Methods and Applications of Linear Models: Regression and the Analysis of Variance seeks to more effectively address the analysis of such models through several important changes. Notable in this new edition: Fully updated and expanded text reflects the most recent developments in the AVE method Rearranged and reorganized discussions of application and theory enhance text’s effectiveness as a teaching tool More than 100 new exercises in the areas of regression and analysis of variance As in the First Edition, the author presents a thorough treatment of the concepts and methods of linear model analysis, and illustrates them with various numerical and conceptual examples, using a data-based approach to development and analysis. Data sets, available on an FTP site, allow readers to apply analytical methods discussed in the book.
Download or read book Intermediate Business Statistics written by Robert Burnham Miller and published by Harcourt Brace College Publishers. This book was released on 1977 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introduction to Mixed Modelling written by Nicholas W. Galwey and published by John Wiley & Sons. This book was released on 2014-08-26 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability and are more realistic than those otherwise obtained. Introduction to Mixed Modelling leads the reader into mixed modelling as a natural extension of two more familiar methods, regression analysis and analysis of variance. It provides practical guidance combined with a clear explanation of the underlying concepts. Like the first edition, this new edition shows diverse applications of mixed models, provides guidance on the identification of random-effect terms, and explains how to obtain and interpret best linear unbiased predictors (BLUPs). It also introduces several important new topics, including the following: Use of the software SAS, in addition to GenStat and R. Meta-analysis and the multiple testing problem. The Bayesian interpretation of mixed models. Including numerous practical exercises with solutions, this book provides an ideal introduction to mixed modelling for final year undergraduate students, postgraduate students and professional researchers. It will appeal to readers from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography. Praise for the first edition: “One of the main strengths of the text is the bridge it provides between traditional analysis of variance and regression models and the more recently developed class of mixed models...Each chapter is well-motivated by at least one carefully chosen example...demonstrating the broad applicability of mixed models in many different disciplines...most readers will likely learn something new, and those previously unfamiliar with mixed models will obtain a solid foundation on this topic.”—Kerrie Nelson University of South Carolina, in American Statistician, 2007
Download or read book Applied Statistics written by Olive Jean Dunn and published by . This book was released on 1987-05-11 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Descriptive statistics. Statistical inference: populations and samples. Inference from a single sample. Samples from two populations. One-way analysis of variance: fixed effects model. Hierarchical or nested design. Two-way analysis of variance: fixed effects model. Three-way analysis of variance: fixed effects model. Factorial designs with each factor at two levels. Variable effects models. Repeated measure designs. Linear regression and correlation. Multiple regression: the fixed X model. Multiple regression and correlation analysis. Analysis of covariance. Data screening.
Download or read book Design and Analysis of Experiments written by Leonard C. Onyiah and published by CRC Press. This book was released on 2008-07-29 with total page 852 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlike other books on the modeling and analysis of experimental data, Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design theory, it also explores regression approaches. Capitalizing on the availability of cutting-edge software, the author uses both manual meth
Download or read book Data Analysis written by Charles M. Judd and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Noted for its model-comparison approach and unified framework based on the general linear model (GLM), this classic text provides readers with a greater understanding of a variety of statistical procedures including analysis of variance (ANOVA) and regression.
