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Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research.Divided into six parts, the handbook begins by establishing notation and terminology.
With the rise of Six Sigma, the use of statistics to analyze and improve processes has once again regained a prominent place in businesses around the world. An increasing number of employees and managers, bestowed with the titles Green Belt, Black Belt, or even Master Black Belts, are asked to apply statistical techniques to analyze and resolve industrial and non-industrial (also known as transactional) problems. These individuals are continuously faced with the daunting task of sorting out the vast array of sophisticated techniques placed at their disposal by an equally impressive array of statistical computer software packages.
Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data. The third edition is a complete rewrite of the book. Factor variables and the margins command changed how the effects of variables can be estimated and interpreted. In addition, the authors’ views on interpretation have evolved. The changes to Stata and to the authors’ views inspired the authors to completely rewrite their popular SPost commands to take advantage of the power of the margins command and the flexibility of factor-variable notation.
A culmination of the author’s many years of consulting and teaching, Design and Analysis of Experiments with SAS provides practical guidance on the computer analysis of experimental data. It connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.
Drawing on a variety of application areas, from pharmaceuticals to machinery, the book presents numerous examples of experiments and exercises that enable students to perform their own experiments.
An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practiceProviding accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format. Featuring smooth transitions between topical coverage, the author carefully justifies the step-by-step process of the most common matrix methods now used in statistical applications, including eigenvalues and eigenvectors; the Moore-Penrose inverse; matrix differentiation; and the distribution of quadratic forms.A
Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors’ collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions.T
What could be better than the bestselling Schaum’s Outline series? For students looking for a quick nuts-and-bolts overview, it would have to be Schaum’s Easy Outline series. Every book in this series is a pared-down, simplified, and tightly focused version of its predecessor.
The current volume, Advances in Latent Variable Mixture Models, contains chapters by all of the speakers who participated in the 2006 CILVR conference, providing not just a snapshot of the event, but more importantly chronicling the state of the art in latent variable mixture model research. The volume starts with an overview chapter by the CILVR conference keynote speaker, Bengt Muthén, offering a "lay of the land" for latent variable mixture models before the volume moves to more specific constellations of topics.
Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methodsFeaturing a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up–to–date coverage of the unique challenges presented in the field of data analysis.T
Consulting and collecting numbers has been a feature of human affairs since antiquity-from the pyramids to tax collection to head counts for military service-but not until the Scientific Revolution in the seventeenth century did social numbers such as births, deaths and marriages begin to be analysed. The Triumph of Numbers explores how numbers have come to assume a leading role in science, in the operations and structure of government, in the analysis of society, in marketing and in many other aspects of daily life.
In recent years several new classes of matrices have been discovered and their structure exploited to design fast and accurate algorithms. In this new reference work, Raf Vandebril, Marc Van Barel, and Nicola Mastronardi present the first comprehensive overview of the mathematical and numerical properties of the family’s newest member: semiseparable matrices.The text is divided into three parts. The first provides some historical background and introduces concepts and definitions concerning structured rank matrices.
Mastering R has never been easierPicking up R can be tough, even for seasoned statisticians and data analysts. "R For Dummies," "2nd Edition" provides a quick and painless way to master all the R you’ll ever need. Requiring no prior programming experience and packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists, data frames, and other R data structures, while learning to interact with other programs, such as Microsoft Excel.
Suitable for self study. Use real examples and real data sets that will be familiar to the audience. Introduction to the bootstrap is included – this is a modern method missing in many other books.
Praise for the "Third Edition ""Future mathematicians, scientists, and engineers should find the book to be an excellent introductory text for coursework or self-study as well as worth its shelf space for reference." –MAA Reviews " Applied Mathematics, Fourth Edition" is a thoroughly updated and revised edition on the applications of modeling and analyzing natural, social, and technological processes. The book covers a wide range of key topics in mathematical methods and modeling and highlights the connections between mathematics and the applied and natural sciences.
This book provides an undergraduate introduction to discrete and continuous-time Markov chains and their applications. A large focus is placed on the first step analysis technique and its applications to average hitting times and ruin probabilities. Classical topics such as recurrence and transience, stationary and limiting distributions, as well as branching processes, are also covered. Two major examples (gambling processes and random walks) are treated in detail from the beginning, before the general theory itself is presented in the subsequent chapters.
Just the critical concepts you need to score high in geometry This practical, friendly guide focuses on critical concepts taught in a typical geometry course, from the properties of triangles, parallelograms, circles, and cylinders, to the skills and strategies you need to write geometry proofs. Geometry Essentials For Dummies is perfect for cramming or doing homework, or as a reference for parents helping kids study for exams. Get down to the basics — get a handle on the basics of geometry, from lines, segments, and angles, to vertices, altitudes, and diagonals Conquer proofs with confidence — follow easy–to–grasp instructions for understanding the components of a formal geometry proof Take triangles in strides — learn how to take in a triangle′s sides, analyze its angles, work through an SAS proof, and apply the Pythagorean Theorem Polish up on polygons — get the lowdown on quadrilaterals and other polygons: their angles, areas, properties, perimeters, and much more Open the book and find: Plain–English explanations of geometry terms Tips for tackling geometry proofs The seven members of the quadrilateral family Straight talk on circles Essential triangle formulas The lowdown on 3–D: spheres, cylinders, prisms, and pyramids Ten things to use as reasons in geometry proofs Learn to: Core concepts about the geometry of shapes and geometry proofs Critical theorems, postulates, and definitions The principles and formulas you need to know
This book developed from classes in mathematical biology taught by the authors over several years at the Technische Universität München. The main themes are modeling principles, mathematical principles for the analysis of these models and model-based analysis of data. The key topics of modern biomathematics are covered: ecology, epidemiology, biochemistry, regulatory networks, neuronal networks and population genetics. A variety of mathematical methods are introduced, ranging from ordinary and partial differential equations to stochastic graph theory and branching processes.
The SAGE Handbook of Performance Studies brings together, in a single volume, reviews of the major research in performance studies and identifies directions for further investigation. It is the only comprehensive collection on the theories, methods, politics, and practices of performance relating to life and culture. Edited by D. Soyini Madison and Judith Hamera, this Handbook serves scholars and students across the disciplines by delineating the scope of the field, the critical and interpretive methods used, and the theoretical and ethical presumptions that guide work in this exciting and growing area.
Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. This new edition of the bestselling Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates the value of a number of these methods with an emphasis on biological applications.
This textbook focuses on three related areas in computational statistics: randomization, bootstrapping, and Monte Carlo methods of inference. The author emphasizes the sampling approach within randomization testing and confidence intervals.
The Seventh Symposium in Applied Mathematics, sponsored by the American Mathematical Society and the Office of Ordnance Research, and devoted to Mathematical Probability and Its Applications, was held at the Polytechnic Institute of Brooklyn on April 14 and 15, 1955. This volume contains the papers (one in abstract form) which were presented at the Symposium. Prolonged consideration by the members of the Program Committee, under the chairmanship of Dr. H. W. Bode, resulted in the decision that the Symposium should be concerned with three principal themes, viz.,
Multidimensional scaling covers a variety of statistical techniques in the area of multivariate data analysis. Geared toward dimensional reduction and graphical representation of data, it arose within the field of the behavioral sciences, but now holds techniques widely used in many disciplines.
With the development of new fitting methods, their increased use in applications, and improved computer languages, the fitting of statistical distributions to data has come a long way since the introduction of the generalized lambda distribution (GLD) in 1969. Handbook of Fitting Statistical Distributions with R presents the latest and best methods, algorithms, and computations for fitting distributions to data. It also provides in-depth coverage of cutting-edge applications.The book begins with commentary by three GLD pioneers: John S.
This is a history of the use of Bayes theoremfrom its discovery by Thomas Bayes to the rise of the statistical competitors in the first part of the twentieth century. The book focuses particularly on the development of one of the fundamental aspects of Bayesian statistics, and in this new edition readers will find new sections on contributors to the theory. In addition, this edition includes amplified discussion of relevant work.
“Elementary Statistics: A Step By Step Approach” is for introductory statistics courses with a basic algebra prerequisite. The book is non-theoretical, explaining concepts intuitively and teaching problem solving through worked examples and step-by-step instructions. In recent editions, Al Bluman has placed more emphasis on conceptual understanding and understanding results, along with increased focus on Excel, MINITAB, and the TI-83 Plus and TI-84 Plus graphing calculators; computing technologies commonly used in such courses.
Showing 1–24 of 53 results