Regression Analysis provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design. Examples and exercises contain real data and graphical illustration for ease of interpretation Outputs from SAS 7, SPSS 7, Excel, and Minitab are used for illustration, but any major statisticalsoftware package will work equally well
Statistical Methods, Fourth Edition, is designed to introduce students to a wide-range of popular and practical statistical techniques. Requiring a minimum of advanced mathematics, it is suitable for undergraduates in statistics, or graduate students in the physical, life, and social sciences. By providing an overview of statistical reasoning, this text equips readers with the insight needed to summarize data, recognize good experimental designs, implement appropriate analyses, and arrive at sound interpretations of statistical results. Includes extensive case studies and exercises drawn from a variety of disciplines Provides practice problems for each chapter with complete solutions Offers new and updated data sets available online Includes recommended data analysis projects with accompanying data sets
This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection. * Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions * Examples contain graphical illustration for ease of interpretation * Intended for use with almost any statistical software * Examples are worked to a logical conclusion, including interpretation of results * A complete Instructor's Manual is available to adopters
Statistical Methods, Fourth Edition, is designed to introduce students to a wide-range of popular and practical statistical techniques. Requiring a minimum of advanced mathematics, it is suitable for undergraduates in statistics, or graduate students in the physical, life, and social sciences. By providing an overview of statistical reasoning, this text equips readers with the insight needed to summarize data, recognize good experimental designs, implement appropriate analyses, and arrive at sound interpretations of statistical results. Includes extensive case studies and exercises drawn from a variety of disciplines Provides practice problems for each chapter with complete solutions Offers new and updated data sets available online Includes recommended data analysis projects with accompanying data sets
This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection. * Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions * Examples contain graphical illustration for ease of interpretation * Intended for use with almost any statistical software * Examples are worked to a logical conclusion, including interpretation of results * A complete Instructor's Manual is available to adopters
Regression Analysis provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design. Examples and exercises contain real data and graphical illustration for ease of interpretation Outputs from SAS 7, SPSS 7, Excel, and Minitab are used for illustration, but any major statisticalsoftware package will work equally well
The new edition of this popular textbook, once again, provides an indispensable guide for the next generation of mineralogists. Designed for use on one- or two-semester courses, this second edition has been thoughtfully reorganised, making it more accessible to students, whilst still being suitable for an advanced mineralogy course. Additions include expanded introductions to many chapters, a new introductory chapter on crystal chemistry, revised figures, and an extended plates section containing beautiful colour photographs. Text boxes include historical background and case studies to engage students, and end-of-chapter questions help them reinforce concepts. With new online resources to support learning and teaching, including laboratory exercises, PowerPoint slides, useful web links and mineral identification tables, this is a sound investment for students in the fields of geology, materials science and environmental science, and a valuable reference for researchers, collectors and anyone interested in minerals.
Features and capabilities of the REG, ANOVA, and GLM procedures are included in this introduction to analysing linear models with the SAS System. This guide shows how to apply the appropriate procedure to data analysis problems and understand PROC GLM output. Other helpful guidelines and discussions cover the following significant areas: Multivariate linear models; lack-of-fit analysis; covariance and heterogeneity of slopes; a classification with both crossed and nested effects; and analysis of variance for balanced data. This fourth edition includes updated examples, new software-related features, and new material, including a chapter on generalised linear models. Version 8 of the SAS System was used to run the SAS code examples in the book. * Provides clear explanations of how to use SAS to analyse linear models * Includes numerous SAS outputs * Includes new chapter on generalised linear models * Uses version 8 of the SAS system This book assists data analysts who use SAS/STAT software to analyse data using regression analysis and analysis of variance. It assumes familiarity with basic SAS concepts such as creating SAS data sets with the DATA step and manipulating SAS data sets with the procedures in base SAS software.
SAS® System for Regression Learn to perform a wide variety of regression analyses using SAS® software with this example-driven revised favorite from SAS Publishing. With this Third Edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics covered include performing linear regression analyses using PROC REG diagnosing and providing remedies for data problems, including outliers and multicollinearity. Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. A helpful discussion of theory is supplied where necessary. Some knowledge of both regression and the SAS System are assumed. New for this edition The Third Edition includes revisions, updated material, and new material. You’ll find new information on using SAS/INSIGHT® software regression with a binary response with emphasis on PROC LOGISTIC nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, using the OUTEST option to produce a data set, and using PROC SCORE to predict another data set.
This book presents the important analytical technique of magnetic microscopy. This method is applied to analyze layered structures with high resolution. This book presents a number of layer-resolving magnetic imaging techniques that have evolved recently. Many exciting new developments in magnetism rely on the ability to independently control the magnetization in two or more magnetic layers in micro- or nanostructures. This in turn requires techniques with the appropriate spatial resolution and magnetic sensitivity. The book begins with an introductory overview, explains then the principles of the various techniques and gives guidance to their use. Selected examples demonstrate the specific strengths of each method. Thus the book is a valuable resource for all scientists and practitioners investigating and applying magnetic layered structures.
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