resampling techniques in regression analysis for model simplification



Douglas Montgomery C. Introduction to Linear Regression Analysis Douglas Montgomery C. Introduction to Linear Regression Analysis Новинка

Douglas Montgomery C. Introduction to Linear Regression Analysis

11323.66 руб.
Praise for the Fourth Edition «As with previous editions, the authors have produced a leading textbook on regression.» —Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model Tests on individual regression coefficients and subsets of coefficients Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.
Sanford Weisberg Applied Linear Regression Sanford Weisberg Applied Linear Regression Новинка

Sanford Weisberg Applied Linear Regression

10498.36 руб.
Praise for the Third Edition «…this is an excellent book which could easily be used as a course text…» —International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition,Applied Linear Regression, Fourth Edition features: Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics Newly added material on topics including testing, ANOVA, and variance assumptions Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.
Hadi Ali S. Regression Analysis by Example Hadi Ali S. Regression Analysis by Example Новинка

Hadi Ali S. Regression Analysis by Example

10346.21 руб.
Praise for the Fourth Edition: «This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable.» —Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including: Surrogate ridge regression Fitting nonlinear models Errors in variables ANOVA for designed experiments Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.
Douglas Montgomery C. Solutions Manual to Accompany Introduction to Linear Regression Analysis Douglas Montgomery C. Solutions Manual to Accompany Introduction to Linear Regression Analysis Новинка

Douglas Montgomery C. Solutions Manual to Accompany Introduction to Linear Regression Analysis

2475.18 руб.
As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.
Iain Pardoe Applied Regression Modeling Iain Pardoe Applied Regression Modeling Новинка

Iain Pardoe Applied Regression Modeling

10031.16 руб.
Praise for the First Edition «The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful.» —The American Statistician Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications. The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling. In addition, the Second Edition features clarification and expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos. With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.
Hesterberg Tim C. Mathematical Statistics with Resampling and R Hesterberg Tim C. Mathematical Statistics with Resampling and R Новинка

Hesterberg Tim C. Mathematical Statistics with Resampling and R

11746.45 руб.
This book bridges the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis Calculation of sampling distributions The Central Limit Theorem Monte Carlo sampling Maximum likelihood estimation and properties of estimators Confidence intervals and hypothesis tests Regression Bayesian methods Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.
Stanley Lemeshow Applied Logistic Regression Stanley Lemeshow Applied Logistic Regression Новинка

Stanley Lemeshow Applied Logistic Regression

10798.6 руб.
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.
Hou Xiaolin Chemistry and Analysis of Radionuclides. Laboratory Techniques and Methodology Hou Xiaolin Chemistry and Analysis of Radionuclides. Laboratory Techniques and Methodology Новинка

Hou Xiaolin Chemistry and Analysis of Radionuclides. Laboratory Techniques and Methodology

10579.59 руб.
Written by chemists for chemists, this is a comprehensive guide to the important radionuclides as well as techniques for their separation and analysis. It introduces readers to the important laboratory techniques and methodologies in the field, providing practical instructions on how to handle nuclear waste and radioactivity in the environment.
Tim Hesterberg C. Mathematical Statistics with Resampling and R Tim Hesterberg C. Mathematical Statistics with Resampling and R Новинка

Tim Hesterberg C. Mathematical Statistics with Resampling and R

9399.76 руб.
This thoroughly updated second edition combines the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. The second edition of Mathematical Statistics with Resampling and R combines modern resampling techniques and mathematical statistics. This book has been classroom-tested to ensure an accessible presentation, uses the powerful and flexible computer language R for data analysis and explores the benefits of modern resampling techniques. This book offers an introduction to permutation tests and bootstrap methods that can serve to motivate classical inference methods. The book strikes a balance between theory, computing, and applications, and the new edition explores additional topics including consulting, paired t test, ANOVA and Google Interview Questions. Throughout the book, new and updated case studies are included representing a diverse range of subjects such as flight delays, birth weights of babies, and telephone company repair times. These illustrate the relevance of the real-world applications of the material. This new edition: • Puts the focus on statistical consulting that emphasizes giving a client an understanding of data and goes beyond typical expectations • Presents new material on topics such as the paired t test, Fisher's Exact Test and the EM algorithm • Offers a new section on «Google Interview Questions» that illustrates statistical thinking • Provides a new chapter on ANOVA • Contains more exercises and updated case studies, data sets, and R code Written for undergraduate students in a mathematical statistics course as well as practitioners and researchers, the second edition of Mathematical Statistics with Resampling and R presents a revised and updated guide for applying the most current resampling techniques to mathematical statistics.
J. Verma P. Sports Research with Analytical Solution using SPSS J. Verma P. Sports Research with Analytical Solution using SPSS Новинка

J. Verma P. Sports Research with Analytical Solution using SPSS

8998.6 руб.
A step-by-step approach to problem-solving techniques using SPSS® in the fields of sports science and physical education Featuring a clear and accessible approach to the methods, processes, and statistical techniques used in sports science and physical education, Sports Research with Analytical Solution using SPSS® emphasizes how to conduct and interpret a range of statistical analysis using SPSS. The book also addresses issues faced by research scholars in these fields by providing analytical solutions to various research problems without reliance on mathematical rigor. Logically arranged to cover both fundamental and advanced concepts, the book presents standard univariate and complex multivariate statistical techniques used in sports research such as multiple regression analysis, discriminant analysis, cluster analysis, and factor analysis. The author focuses on the treatment of various parametric and nonparametric statistical tests, which are shown through the techniques and interpretations of the SPSS outputs that are generated for each analysis. Sports Research with Analytical Solution using SPSS® also features: Numerous examples and case studies to provide readers with practical applications of the analytical concepts and techniques Plentiful screen shots throughout to help demonstrate the implementation of SPSS outputs Illustrative studies with simulated realistic data to clarify the analytical techniques covered End-of-chapter short answer questions, multiple choice questions, assignments, and practice exercises to help build a better understanding of the presented concepts A companion website with associated SPSS data files and PowerPoint® presentations for each chapter Sports Research with Analytical Solution using SPSS® is an excellent textbook for upper-undergraduate, graduate, and PhD-level courses in research methods, kinesiology, sports science, medicine, nutrition, health education, and physical education. The book is also an ideal reference for researchers and professionals in the fields of sports research, sports science, physical education, and social sciences, as well as anyone interested in learning SPSS.
Chrysseis Caroni First Hitting Time Regression Models. Lifetime Data Analysis Based on Underlying Stochastic Processes Chrysseis Caroni First Hitting Time Regression Models. Lifetime Data Analysis Based on Underlying Stochastic Processes Новинка

Chrysseis Caroni First Hitting Time Regression Models. Lifetime Data Analysis Based on Underlying Stochastic Processes

8248.71 руб.
This book aims to promote regression methods for analyzing lifetime (or time-to-event) data that are based on a representation of the underlying process, and are therefore likely to offer greater scientific insight compared to purely empirical methods. In contrast to the rich statistical literature, the regression methods actually employed in lifetime data analysis are limited, particularly in the biomedical field where D. R. Cox’s famous semi-parametric proportional hazards model predominates. Practitioners should become familiar with more flexible models. The first hitting time regression models (or threshold regression) presented here represent observed events as the outcome of an underlying stochastic process. One example is death occurring when the patient’s health status falls to zero, but the idea has wide applicability – in biology, engineering, banking and finance, and elsewhere. The central topic is the model based on an underlying Wiener process, leading to lifetimes following the inverse Gaussian distribution. Introducing time-varying covariates and many other extensions are considered. Various applications are presented in detail.
Siddhartha Bhattacharyya Hybrid Intelligence for Image Analysis and Understanding Siddhartha Bhattacharyya Hybrid Intelligence for Image Analysis and Understanding Новинка

Siddhartha Bhattacharyya Hybrid Intelligence for Image Analysis and Understanding

10498.36 руб.
A synergy of techniques on hybrid intelligence for real-life image analysis Hybrid Intelligence for Image Analysis and Understanding brings together research on the latest results and progress in the development of hybrid intelligent techniques for faithful image analysis and understanding. As such, the focus is on the methods of computational intelligence, with an emphasis on hybrid intelligent methods applied to image analysis and understanding. The book offers a diverse range of hybrid intelligence techniques under the umbrellas of image thresholding, image segmentation, image analysis and video analysis. Key features: Provides in-depth analysis of hybrid intelligent paradigms. Divided into self-contained chapters. Provides ample case studies, illustrations and photographs of real-life examples to illustrate findings and applications of different hybrid intelligent paradigms. Offers new solutions to recent problems in computer science, specifically in the application of hybrid intelligent techniques for image analysis and understanding, using well-known contemporary algorithms. The book is essential reading for lecturers, researchers and graduate students in electrical engineering and computer science.
R. Rhinehart Russell Nonlinear Regression Modeling for Engineering Applications. Modeling, Model Validation, and Enabling Design of Experiments R. Rhinehart Russell Nonlinear Regression Modeling for Engineering Applications. Modeling, Model Validation, and Enabling Design of Experiments Новинка

R. Rhinehart Russell Nonlinear Regression Modeling for Engineering Applications. Modeling, Model Validation, and Enabling Design of Experiments

9579.15 руб.
Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineering applications related to diagnosis, design, and optimization. First, we postulate a mechanism, then derive a model grounded in that mechanistic understanding. If the model does not fit the data, our understanding of the mechanism was wrong or incomplete. Patterns in the residuals can guide model improvement. Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications. This book details methods of nonlinear regression, computational algorithms,model validation, interpretation of residuals, and useful experimental design. The focus is on practical applications, with relevant methods supported by fundamental analysis. This book will assist either the academic or industrial practitioner to properly classify the system, choose between the various available modeling options and regression objectives, design experiments to obtain data capturing critical system behaviors, fit the model parameters based on that data, and statistically characterize the resulting model. The author has used the material in the undergraduate unit operations lab course and in advanced control applications.
Samprit Chatterjee Handbook of Regression Analysis Samprit Chatterjee Handbook of Regression Analysis Новинка

Samprit Chatterjee Handbook of Regression Analysis

10273.54 руб.
A Comprehensive Account for Data Analysts of the Methods and Applications of Regression Analysis. Written by two established experts in the field, the purpose of the Handbook of Regression Analysis is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of regression methods, but it has been deliberately written at an accessible level. The handbook provides a quick and convenient reference or “refresher” on ideas and methods that are useful for the effective analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (including linear, binary logistic, multinomial logistic, count, and nonlinear regression models). Theory underlying the methodology is presented when it advances conceptual understanding and is always supplemented by hands-on examples. References are supplied for readers wanting more detailed material on the topics discussed in the book. R code and data for all of the analyses described in the book are available via an author-maintained website. «I enjoyed the presentation of the Handbook, and I would be happy to recommend this nice handy book as a reference to my students. The clarity of the writing and proper choices of examples allows the presentations ofmany statisticalmethods shine. The quality of the examples at the end of each chapter is a strength. They entail explanations of the resulting R outputs and successfully guide readers to interpret them.» American Statistician
François Auger Signal Processing with Free Software. Practical Experiments François Auger Signal Processing with Free Software. Practical Experiments Новинка

François Auger Signal Processing with Free Software. Practical Experiments

4900.84 руб.
An ideal resource for students, industrial engineers, and researchers, Signal Processing with Free Software Practical Experiments presents practical experiments in signal processing using free software. The text introduces elementary signals through elementary waveform, signal storage files and elementary operations on signals and then presents the first tools to signal analysis such as temporal and frequency characteristics leading to Time-frequency analysis. Non-parametric spectral analysis is also discussed as well as signal processing through sampling, resampling, quantification, and analog and digital filtering. Table of Contents: 1. Generation of Elementary Signals. Generation of Elementary Waveform. – Elementary Operations on the Signals. – Format of Signal Storage Files. 2. First tools of Signal Analysis. Measurement of Temporal and Frequency Characteristics of a Signal. Time-Frequency Analysis of a Signal. 3. Non-parametric Spectral Analysis. 4. Signal Processing. Sampling. – Resampling. – Quantification. – “Analog” Filtering. Digital Filtering
Alan Agresti Categorical Data Analysis Alan Agresti Categorical Data Analysis Новинка

Alan Agresti Categorical Data Analysis

11668.66 руб.
Praise for the Second Edition «A must-have book for anyone expecting to do research and/or applications in categorical data analysis.» —Statistics in Medicine «It is a total delight reading this book.» —Pharmaceutical Research «If you do any analysis of categorical data, this is an essential desktop reference.» —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
Marc Paolella S. Linear Models and Time-Series Analysis. Regression, ANOVA, ARMA and GARCH Marc Paolella S. Linear Models and Time-Series Analysis. Regression, ANOVA, ARMA and GARCH Новинка

Marc Paolella S. Linear Models and Time-Series Analysis. Regression, ANOVA, ARMA and GARCH

11668.66 руб.
A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.
Kupe Kupersmith Business Analysis For Dummies Kupe Kupersmith Business Analysis For Dummies Новинка

Kupe Kupersmith Business Analysis For Dummies

1749.65 руб.
Your go-to guide on business analysis Business analysis refers to the set of tasks and activities that help companies determine their objectives for meeting certain opportunities or addressing challenges and then help them define solutions to meet those objectives. Those engaged in business analysis are charged with identifying the activities that enable the company to define the business problem or opportunity, define what the solutions looks like, and define how it should behave in the end. As a BA, you lay out the plans for the process ahead. Business Analysis For Dummies is the go to reference on how to make the complex topic of business analysis easy to understand. Whether you are new or have experience with business analysis, this book gives you the tools, techniques, tips and tricks to set your project’s expectations and on the path to success. Offers guidance on how to make an impact in your organization by performing business analysis Shows you the tools and techniques to be an effective business analysis professional Provides a number of examples on how to perform business analysis regardless of your role If you're interested in learning about the tools and techniques used by successful business analysis professionals, Business Analysis For Dummies has you covered.
Clifton A. Ericson, II Hazard Analysis Techniques for System Safety Clifton A. Ericson, II Hazard Analysis Techniques for System Safety Новинка

Clifton A. Ericson, II Hazard Analysis Techniques for System Safety

9373.54 руб.
Explains in detail how to perform the most commonly used hazard analysis techniques with numerous examples of practical applications • Includes new chapters on Concepts of Hazard Recognition, Environmental Hazard Analysis, Process Hazard Analysis, Test Hazard Analysis, and Job Hazard Analysis • Updated text covers introduction, theory, and detailed description of many different hazard analysis techniques and explains in detail how to perform them as well as when and why to use each technique • Describes the components of a hazard and how to recognize them during an analysis • Contains detailed examples that apply the methodology to everyday problems
Elisa Lee T. Statistical Methods for Survival Data Analysis Elisa Lee T. Statistical Methods for Survival Data Analysis Новинка

Elisa Lee T. Statistical Methods for Survival Data Analysis

10048.72 руб.
Praise for the Third Edition “. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject.” —Statistics in Medical Research Updated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, Fourth Edition continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences. Emphasizing the latest research and providing the most up-to-date information regarding software applications in the field, Statistical Methods for Survival Data Analysis, Fourth Edition also includes: Marginal and random effect models for analyzing correlated censored or uncensored data Multiple types of two-sample and K-sample comparison analysis Updated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time models Expanded coverage of the Cox proportional hazards model Exercises at the end of each chapter to deepen knowledge of the presented material Statistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis. The book is also an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.
David Rosenthal A. Statistics for Health Care Management and Administration. Working with Excel David Rosenthal A. Statistics for Health Care Management and Administration. Working with Excel Новинка

David Rosenthal A. Statistics for Health Care Management and Administration. Working with Excel

8248.71 руб.
The must-have statistics guide for students of health services Statistics for Health Care Management and Administration is a unique and invaluable resource for students of health care administration and public health. The book introduces students to statistics within the context of health care, focusing on the major data and analysis techniques used in the field. All hands-on instruction makes use of Excel, the most common spreadsheet software that is ubiquitous in the workplace. This new third edition has been completely retooled, with new content on proportions, ANOVA, linear regression, chi-squares, and more, Step-by-step instructions in the latest version of Excel and numerous annotated screen shots make examples easy to follow and understand. Familiarity with statistical methods is essential for health services professionals and researchers, who must understand how to acquire, handle, and analyze data. This book not only helps students develop the necessary data analysis skills, but it also boosts familiarity with important software that employers will be looking for. Learn the basics of statistics in the context of Excel Understand how to acquire data and display it for analysis Master various tests including probability, regression, and more Turn test results into usable information with proper analysis Statistics for Health Care Management and Administration gets students off to a great start by introducing statistics in the workplace context from the very beginning.
Simant Upreti R. Process Modeling and Simulation for Chemical Engineers. Theory and Practice Simant Upreti R. Process Modeling and Simulation for Chemical Engineers. Theory and Practice Новинка

Simant Upreti R. Process Modeling and Simulation for Chemical Engineers. Theory and Practice

8623.66 руб.
This book provides a rigorous treatment of the fundamental concepts and techniques involved in process modeling and simulation. The book allows the reader to: (i) Get a solid grasp of “under-the-hood” mathematical results (ii) Develop models of sophisticated processes (iii) Transform models to different geometries and domains as appropriate (iv) Utilize various model simplification techniques (v) Learn simple and effective computational methods for model simulation (vi) Intensify the effectiveness of their research Modeling and Simulation for Chemical Engineers: Theory and Practice begins with an introduction to the terminology of process modeling and simulation. Chapters 2 and 3 cover fundamental and constitutive relations, while Chapter 4 on model formulation builds on these relations. Chapters 5 and 6 introduce the advanced techniques of model transformation and simplification. Chapter 7 deals with model simulation, and the final chapter reviews important mathematical concepts. Presented in a methodical, systematic way, this book is suitable as a self-study guide or as a graduate reference, and includes examples, schematics and diagrams to enrich understanding. End of chapter problems with solutions and computer software available online at www.wiley.com/go/upreti/pms_for_chemical_engineers are designed to further stimulate readers to apply the newly learned concepts.
Caroli Sergio Analytical Techniques for Clinical Chemistry. Methods and Applications Caroli Sergio Analytical Techniques for Clinical Chemistry. Methods and Applications Новинка

Caroli Sergio Analytical Techniques for Clinical Chemistry. Methods and Applications

16491.71 руб.
Discover how analytical chemistry supports the latest clinical research This book details the role played by analytical chemistry in fostering clinical research. Readers will discover how a broad range of analytical techniques support all phases of clinical research, from early stages to the implementation of practical applications. Moreover, the contributing authors' careful step-by-step guidance enables readers to better understand standardized techniques and steer clear of everyday problems that can arise in the lab. Analytical Techniques for Clinical Chemistry opens with an overview of the legal and regulatory framework governing clinical lab analysis. Next, it details the latest progress in instrumentation and applications in such fields as biomonitoring, diagnostics, food quality, biomarkers, pharmaceuticals, and forensics. Comprised of twenty-five chapters divided into three sections exploring Fundamentals, Selected Applications, and Future Trends, the book covers such critical topics as: Uncertainty in clinical chemistry measurements Metal toxicology in clinical, forensic, and chemical pathology Role of analytical chemistry in the safety of drug therapy Atomic spectrometric techniques for the analysis of clinical samples Biosensors for drug analysis Use of X-ray techniques in medical research Each chapter is written by one or more leading pioneers and experts in analytical chemistry. Contributions are based on a thorough review and analysis of the current literature as well as the authors' own firsthand experiences in the lab. References at the end of each chapter serve as a gateway to the literature, enabling readers to explore individual topics in greater depth. Presenting the latest achievements and challenges in the field, Analytical Techniques for Clinical Chemistry sets the foundation for future advances in laboratory research techniques.
Despina Tsipi Mass Spectrometry for the Analysis of Pesticide Residues and their Metabolites Despina Tsipi Mass Spectrometry for the Analysis of Pesticide Residues and their Metabolites Новинка

Despina Tsipi Mass Spectrometry for the Analysis of Pesticide Residues and their Metabolites

9373.54 руб.
Provides an overview of the use of mass spectrometry (MS) for the analysis of pesticide residues and their metabolites. Presents state of the-art MS techniques for the identification of pesticides and their transformation products in food and environment Covers important advances in MS techniques including MS instrumentation and chromatographic separations (e.g. UPLC, HILIC, comprehensive GCxGC) and applications Illustrates the main sample preparation techniques (SPE, QuEChERS, microextraction) used in combination with MS for the analysis of pesticides Describes various established and new ionization techniques as well as the main MS platforms, software tools and mass spectral libraries
Cristina Davino Quantile Regression. Theory and Applications Cristina Davino Quantile Regression. Theory and Applications Новинка

Cristina Davino Quantile Regression. Theory and Applications

8020.33 руб.
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data. Quantile Regression: Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods. Delivers a balance between methodolgy and application Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing. Features a supporting website (www.wiley.com/go/quantile_regression) hosting datasets along with R, Stata and SAS software code. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book.
Raynor Mark W. Trace Analysis of Specialty and Electronic Gases Raynor Mark W. Trace Analysis of Specialty and Electronic Gases Новинка

Raynor Mark W. Trace Analysis of Specialty and Electronic Gases

8319.76 руб.
Explores the latest advances and applications of specialty and electronic gas analysis The semiconductor industry depends upon a broad range of instrumental techniques in order to detect and analyze impurities that may be present in specialty and electronic gases, including permanent gases, water vapor, reaction by-products, and metal species. Trace Analysis of Specialty and Electronic Gases draws together all the latest advances in analytical chemistry, providing researchers with both the theory and the operating principles of the full spectrum of instrumental techniques available for specialty and electronic gas analysis. Moreover, the book details the advantages and disadvantages of each technique, steering readers away from common pitfalls. Featuring contributions from leading analytical and industrial chemists, Trace Analysis of Specialty and Electronic Gases covers a wide range of practical industrial applications. The book begins with the historical development of gas analysis and then focuses on particular subjects or techniques such as: Metals sampling and ICP-MS analysis Improvements in FTIR spectroscopy Water vapor analysis techniques New infrared laser absorption spectroscopy approaches GC/MS, GC/AED, and GC-ICP-MS techniques Gas chromatography columns Atmospheric pressure ionization mass spectrometry Lastly, the book examines gas mixtures and standards that are critical for instrument calibration. There are also two appendices offering information on fittings and material compatibility. With its thorough review of the literature and step-by-step guidance, Trace Analysis of Specialty and Electronic Gases enables researchers to take full advantage of the latest advances in gas analysis. Although the book's focus is the semiconductor and electronics industry, analytical chemists in other industries facing challenges with such issues as detection selectivity and sensitivity, matrix gas interference, and materials compatibility will also discover plenty of useful analytical approaches and techniques.
Bradley Huitema The Analysis of Covariance and Alternatives. Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies Bradley Huitema The Analysis of Covariance and Alternatives. Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies Новинка

Bradley Huitema The Analysis of Covariance and Alternatives. Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies

11279.71 руб.
A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.
Basel Abu-Jamous Integrative Cluster Analysis in Bioinformatics Basel Abu-Jamous Integrative Cluster Analysis in Bioinformatics Новинка

Basel Abu-Jamous Integrative Cluster Analysis in Bioinformatics

10498.36 руб.
Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review of clustering analysis in bioinformatics from the fundamentals through to state-of-the-art techniques and applications. Key Features: Offers a contemporary review of clustering methods and applications in the field of bioinformatics, with particular emphasis on gene expression analysis Provides an excellent introduction to molecular biology with computer scientists and information engineering researchers in mind, laying out the basic biological knowledge behind the application of clustering analysis techniques in bioinformatics Explains the structure and properties of many types of high-throughput datasets commonly found in biological studies Discusses how clustering methods and their possible successors would be used to enhance the pace of biological discoveries in the future Includes a companion website hosting a selected collection of codes and links to publicly available datasets
Pawel Cichosz Data Mining Algorithms. Explained Using R Pawel Cichosz Data Mining Algorithms. Explained Using R Новинка

Pawel Cichosz Data Mining Algorithms. Explained Using R

5999.06 руб.
Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.
Nikolaos Limnios Statistical Models and Methods for Reliability and Survival Analysis Nikolaos Limnios Statistical Models and Methods for Reliability and Survival Analysis Новинка

Nikolaos Limnios Statistical Models and Methods for Reliability and Survival Analysis

14022.96 руб.
Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts – Statistical Models and Methods, Statistical Models and Methods in Survival Analysis, and Reliability and Maintenance. The book is intended for researchers interested in statistical methodology and models useful in survival analysis, system reliability and statistical testing for censored and non-censored data.
Bendat Julius S. Random Data. Analysis and Measurement Procedures Bendat Julius S. Random Data. Analysis and Measurement Procedures Новинка

Bendat Julius S. Random Data. Analysis and Measurement Procedures

14391.35 руб.
A timely update of the classic book on the theory and application of random data analysis First published in 1971, Random Data served as an authoritative book on the analysis of experimental physical data for engineering and scientific applications. This Fourth Edition features coverage of new developments in random data management and analysis procedures that are applicable to a broad range of applied fields, from the aerospace and automotive industries to oceanographic and biomedical research. This new edition continues to maintain a balance of classic theory and novel techniques. The authors expand on the treatment of random data analysis theory, including derivations of key relationships in probability and random process theory. The book remains unique in its practical treatment of nonstationary data analysis and nonlinear system analysis, presenting the latest techniques on modern data acquisition, storage, conversion, and qualification of random data prior to its digital analysis. The Fourth Edition also includes: A new chapter on frequency domain techniques to model and identify nonlinear systems from measured input/output random data New material on the analysis of multiple-input/single-output linear models The latest recommended methods for data acquisition and processing of random data Important mathematical formulas to design experiments and evaluate results of random data analysis and measurement procedures Answers to the problem in each chapter Comprehensive and self-contained, Random Data, Fourth Edition is an indispensible book for courses on random data analysis theory and applications at the upper-undergraduate and graduate level. It is also an insightful reference for engineers and scientists who use statistical methods to investigate and solve problems with dynamic data.
Xian Liu Survival Analysis. Models and Applications Xian Liu Survival Analysis. Models and Applications Новинка

Xian Liu Survival Analysis. Models and Applications

8630.92 руб.
Survival analysis concerns sequential occurrences of events governed by probabilistic laws. Recent decades have witnessed many applications of survival analysis in various disciplines. This book introduces both classic survival models and theories along with newly developed techniques. Readers will learn how to perform analysis of survival data by following numerous empirical illustrations in SAS. Survival Analysis: Models and Applications: Presents basic techniques before leading onto some of the most advanced topics in survival analysis. Assumes only a minimal knowledge of SAS whilst enabling more experienced users to learn new techniques of data input and manipulation. Provides numerous examples of SAS code to illustrate each of the methods, along with step-by-step instructions to perform each technique. Highlights the strengths and limitations of each technique covered. Covering a wide scope of survival techniques and methods, from the introductory to the advanced, this book can be used as a useful reference book for planners, researchers, and professors who are working in settings involving various lifetime events. Scientists interested in survival analysis should find it a useful guidebook for the incorporation of survival data and methods into their projects.
Xian Liu Survival Analysis. Models and Applications Xian Liu Survival Analysis. Models and Applications Новинка

Xian Liu Survival Analysis. Models and Applications

8319.86 руб.
Survival analysis concerns sequential occurrences of events governed by probabilistic laws. Recent decades have witnessed many applications of survival analysis in various disciplines. This book introduces both classic survival models and theories along with newly developed techniques. Readers will learn how to perform analysis of survival data by following numerous empirical illustrations in SAS. Survival Analysis: Models and Applications: Presents basic techniques before leading onto some of the most advanced topics in survival analysis. Assumes only a minimal knowledge of SAS whilst enabling more experienced users to learn new techniques of data input and manipulation. Provides numerous examples of SAS code to illustrate each of the methods, along with step-by-step instructions to perform each technique. Highlights the strengths and limitations of each technique covered. Covering a wide scope of survival techniques and methods, from the introductory to the advanced, this book can be used as a useful reference book for planners, researchers, and professors who are working in settings involving various lifetime events. Scientists interested in survival analysis should find it a useful guidebook for the incorporation of survival data and methods into their projects.
James Schott R. Matrix Analysis for Statistics James Schott R. Matrix Analysis for Statistics Новинка

James Schott R. Matrix Analysis for Statistics

9373.54 руб.
An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice Providing 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. An ideal introduction to matrix analysis theory and practice, Matrix Analysis for Statistics, Third Edition features: • New chapter or section coverage on inequalities, oblique projections, and antieigenvalues and antieigenvectors • Additional problems and chapter-end practice exercises at the end of each chapter • Extensive examples that are familiar and easy to understand • Self-contained chapters for flexibility in topic choice • Applications of matrix methods in least squares regression and the analyses of mean vectors and covariance matrices Matrix Analysis for Statistics, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses on matrix methods, multivariate analysis, and linear models. The book is also an excellent reference for research professionals in applied statistics. James R. Schott, PhD, is Professor in the Department of Statistics at the University of Central Florida. He has published numerous journal articles in the area of multivariate analysis. Dr. Schott’s research interests include multivariate analysis, analysis of covariance and correlation matrices, and dimensionality reduction techniques.
Oleg Wasynczuk Analysis of Electric Machinery and Drive Systems Oleg Wasynczuk Analysis of Electric Machinery and Drive Systems Новинка

Oleg Wasynczuk Analysis of Electric Machinery and Drive Systems

9673.78 руб.
Introducing a new edition of the popular reference on machine analysis Now in a fully revised and expanded edition, this widely used reference on machine analysis boasts many changes designed to address the varied needs of engineers in the electric machinery, electric drives, and electric power industries. The authors draw on their own extensive research efforts, bringing all topics up to date and outlining a variety of new approaches they have developed over the past decade. Focusing on reference frame theory that has been at the core of this work since the first edition, this volume goes a step further, introducing new material relevant to machine design along with numerous techniques for making the derivation of equations more direct and easy to use. Coverage includes: Completely new chapters on winding functions and machine design that add a significant dimension not found in any other text A new formulation of machine equations for improving analysis and modeling of machines coupled to power electronic circuits Simplified techniques throughout, from the derivation of torque equations and synchronous machine analysis to the analysis of unbalanced operation A unique generalized approach to machine parameters identification A first-rate resource for engineers wishing to master cutting-edge techniques for machine analysis, Analysis of Electric Machinery and Drive Systems is also a highly useful guide for students in the field.
Eric Johnson R. Handbook of Decision Analysis Eric Johnson R. Handbook of Decision Analysis Новинка

Eric Johnson R. Handbook of Decision Analysis

12373.78 руб.
A ONE-OF-A-KIND GUIDE TO THE BEST PRACTICES IN DECISION ANALYSIS Decision analysis provides powerful tools for addressing complex decisions that involve uncertainty and multiple objectives, yet most training materials on the subject overlook the soft skills that are essential for success in the field. This unique resource fills this gap in the decision analysis literature and features both soft personal/interpersonal skills and the hard technical skills involving mathematics and modeling. Readers will learn how to identify and overcome the numerous challenges of decision making, choose the appropriate decision process, lead and manage teams, and create value for their organization. Performing modeling analysis, assessing risk, and implementing decisions are also addressed throughout. Additional features include: Key insights gleaned from decision analysis applications and behavioral decision analysis research Integrated coverage of the techniques of single- and multiple-objective decision analysis Multiple qualitative and quantitative techniques presented for each key decision analysis task Three substantive real-world case studies illustrating diverse strategies for dealing with the challenges of decision making Extensive references for mathematical proofs and advanced topics The Handbook of Decision Analysis is an essential reference for academics and practitioners in various fields including business, operations research, engineering, and science. The book also serves as a supplement for courses at the upper-undergraduate and graduate levels.
Uversky Vladimir Instrumental Analysis of Intrinsically Disordered Proteins. Assessing Structure and Conformation Uversky Vladimir Instrumental Analysis of Intrinsically Disordered Proteins. Assessing Structure and Conformation Новинка

Uversky Vladimir Instrumental Analysis of Intrinsically Disordered Proteins. Assessing Structure and Conformation

14157.98 руб.
Instrumental techniques for analyzing intrinsically disordered proteins The recently recognized phenomenon of protein intrinsic disorder is gaining significant interest among researchers, especially as the number of proteins and protein domains that have been shown to be intrinsically disordered rapidly grows. The first reference to tackle this little-documented area, Instrumental Analysis of Intrinsically Disordered Proteins: Assessing Structure and Conformation provides researchers with a much-needed, comprehensive summary of recent achievements in the methods for structural characterization of intrinsically disordered proteins (IDPs). Chapters discuss: Assessment of IDPs in the living cell Spectroscopic techniques for the analysis of IDPs, including NMR and EPR spectroscopies, FTIR, circular dichroism, fluorescence spectroscopy, vibrational methods, and single-molecule analysis Single-molecule techniques applied to the study of IDPs Assessment of IDP size and shape Tools for the analysis of IDP conformational stability Mass spectrometry Approaches for expression and purification of IDPs With contributions from an international selection of leading researchers, Instrumental Analysis of Intrinsically Disordered Proteins: Assessing Structure and Conformation fills an important need in a rapidly growing field. It is required reading for biochemists, biophysicists, molecular biologists, geneticists, cell biologists, physiologists, and specialists in drug design and development, proteomics, and molecular medicine with an interest in proteins and peptides.
DeWayne Derryberry R. Basic Data Analysis for Time Series with R DeWayne Derryberry R. Basic Data Analysis for Time Series with R Новинка

DeWayne Derryberry R. Basic Data Analysis for Time Series with R

8923.18 руб.
Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals. Focusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC). In addition, Basic Data Analysis for Time Series with R also features: Real-world examples to provide readers with practical hands-on experience Multiple R software subroutines employed with graphical displays Numerous exercise sets intended to support readers understanding of the core concepts Specific chapters devoted to the analysis of the Wolf sunspot number data and the Vostok ice core data sets
Jae Lee K. Statistical Bioinformatics. For Biomedical and Life Science Researchers Jae Lee K. Statistical Bioinformatics. For Biomedical and Life Science Researchers Новинка

Jae Lee K. Statistical Bioinformatics. For Biomedical and Life Science Researchers

9953.37 руб.
This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.
Bryan Denham E. Categorical Statistics for Communication Research Bryan Denham E. Categorical Statistics for Communication Research Новинка

Bryan Denham E. Categorical Statistics for Communication Research

6745.39 руб.
Categorical Statistics for CommunicationResearch presents scholars with a discipline-specific guide to categorical data analysis. The text blends necessary background information and formulas for statistical procedures with data analyses illustrating techniques such as log- linear modeling and logistic regression analysis. Provides techniques for analyzing categorical data from a communication studies perspective Provides an accessible presentation of techniques for analyzing categorical data for communication scholars and other social scientists working at the advanced undergraduate and graduate teaching levels Illustrated with examples from different types of communication research such as health, political and sports communication and entertainment Includes exercises at the end of each chapter and a companion website containing exercise answers and chapter-by-chapter PowerPoint slides
Ilya Narsky Statistical Analysis Techniques in Particle Physics. Fits, Density Estimation and Supervised Learning Ilya Narsky Statistical Analysis Techniques in Particle Physics. Fits, Density Estimation and Supervised Learning Новинка

Ilya Narsky Statistical Analysis Techniques in Particle Physics. Fits, Density Estimation and Supervised Learning

8923.18 руб.
Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.
Kristin Jarman H. The Art of Data Analysis. How to Answer Almost Any Question Using Basic Statistics Kristin Jarman H. The Art of Data Analysis. How to Answer Almost Any Question Using Basic Statistics Новинка

Kristin Jarman H. The Art of Data Analysis. How to Answer Almost Any Question Using Basic Statistics

4978.63 руб.
A friendly and accessible approach to applying statistics in the real world With an emphasis on critical thinking, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics presents fun and unique examples, guides readers through the entire data collection and analysis process, and introduces basic statistical concepts along the way. Leaving proofs and complicated mathematics behind, the author portrays the more engaging side of statistics and emphasizes its role as a problem-solving tool. In addition, light-hearted case studies illustrate the application of statistics to real data analyses, highlighting the strengths and weaknesses of commonly used techniques. Written for the growing academic and industrial population that uses statistics in everyday life, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics highlights important issues that often arise when collecting and sifting through data. Featured concepts include: • Descriptive statistics • Analysis of variance • Probability and sample distributions • Confidence intervals • Hypothesis tests • Regression • Statistical correlation • Data collection • Statistical analysis with graphs Fun and inviting from beginning to end, The Art of Data Analysis is an ideal book for students as well as managers and researchers in industry, medicine, or government who face statistical questions and are in need of an intuitive understanding of basic statistical reasoning.
McCormick Norman J. Risk and Safety Analysis of Nuclear Systems McCormick Norman J. Risk and Safety Analysis of Nuclear Systems Новинка

McCormick Norman J. Risk and Safety Analysis of Nuclear Systems

10657.38 руб.
The book has been developed in conjunction with NERS 462, a course offered every year to seniors and graduate students in the University of Michigan NERS program. The first half of the book covers the principles of risk analysis, the techniques used to develop and update a reliability data base, the reliability of multi-component systems, Markov methods used to analyze the unavailability of systems with repairs, fault trees and event trees used in probabilistic risk assessments (PRAs), and failure modes of systems. All of this material is general enough that it could be used in non-nuclear applications, although there is an emphasis placed on the analysis of nuclear systems. The second half of the book covers the safety analysis of nuclear energy systems, an analysis of major accidents and incidents that occurred in commercial nuclear plants, applications of PRA techniques to the safety analysis of nuclear power plants (focusing on a major PRA study for five nuclear power plants), practical PRA examples, and emerging techniques in the structure of dynamic event trees and fault trees that can provide a more realistic representation of complex sequences of events. The book concludes with a discussion on passive safety features of advanced nuclear energy systems under development and approaches taken for risk-informed regulations for nuclear plants.
Christophe Basso P. Linear Circuit Transfer Functions. An Introduction to Fast Analytical Techniques Christophe Basso P. Linear Circuit Transfer Functions. An Introduction to Fast Analytical Techniques Новинка

Christophe Basso P. Linear Circuit Transfer Functions. An Introduction to Fast Analytical Techniques

9749.19 руб.
Linear Circuit Transfer Functions: An introduction to Fast Analytical Techniques teaches readers how to determine transfer functions of linear passive and active circuits by applying Fast Analytical Circuits Techniques. Building on their existing knowledge of classical loop/nodal analysis, the book improves and expands their skills to unveil transfer functions in a swift and efficient manner. Starting with simple examples, the author explains step-by-step how expressing circuits time constants in different configurations leads to writing transfer functions in a compact and insightful way. By learning how to organize numerators and denominators in the fastest possible way, readers will speed-up analysis and predict the frequency response of simple to complex circuits. In some cases, they will be able to derive the final expression by inspection, without writing a line of algebra. Key features: Emphasizes analysis through employing time constant-based methods discussed in other text books but not widely used or explained. Develops current techniques on transfer functions, to fast analytical techniques leading to low-entropy transfer functions immediately exploitable for analysis purposes. Covers calculation techniques pertinent to different fields, electrical, electronics, signal processing etc. Describes how a technique is applied and demonstrates this through real design examples. All Mathcad® files used in examples and problems are freely available for download. An ideal reference for electronics or electrical engineering professionals as well as BSEE and MSEE students, this book will help teach them how to: become skilled in the art of determining transfer function by using less algebra and obtaining results in a more effectual way; gain insight into a circuit’s operation by understanding how time constants rule dynamic responses; apply Fast Analytical Techniques to simple and complicated circuits, passive or active and be more efficient at solving problems.
Ion Mandoiu Computational Methods for Next Generation Sequencing Data Analysis Ion Mandoiu Computational Methods for Next Generation Sequencing Data Analysis Новинка

Ion Mandoiu Computational Methods for Next Generation Sequencing Data Analysis

8998.6 руб.
Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.
Peter C. Bruce Introductory Statistics and Analytics. A Resampling Perspective Peter C. Bruce Introductory Statistics and Analytics. A Resampling Perspective Новинка

Peter C. Bruce Introductory Statistics and Analytics. A Resampling Perspective

5620.57 руб.
Concise, thoroughly class-tested primer that features basic statistical concepts in the concepts in the context of analytics, resampling, and the bootstrap A uniquely developed presentation of key statistical topics, Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic probability and statistics. Originally class-tested at one of the first online learning companies in the discipline, www.statistics.com, the book primarily focuses on applications of statistical concepts developed via resampling, with a background discussion of mathematical theory. This feature stresses statistical literacy and understanding, which demonstrates the fundamental basis for statistical inference and demystifies traditional formulas. The book begins with illustrations that have the essential statistical topics interwoven throughout before moving on to demonstrate the proper design of studies. Meeting all of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) requirements for an introductory statistics course, Introductory Statistics and Analytics: A Resampling Perspective also includes: Over 300 “Try It Yourself” exercises and intermittent practice questions, which challenge readers at multiple levels to investigate and explore key statistical concepts Numerous interactive links designed to provide solutions to exercises and further information on crucial concepts Linkages that connect statistics to the rapidly growing field of data science Multiple discussions of various software systems, such as Microsoft Office Excel®, StatCrunch, and R, to develop and analyze data Areas of concern and/or contrasting points-of-view indicated through the use of “Caution” icons Introductory Statistics and Analytics: A Resampling Perspective is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.
Sabine Landau Cluster Analysis Sabine Landau Cluster Analysis Новинка

Sabine Landau Cluster Analysis

7123.89 руб.
Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics. This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis. Key Features: • Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis. • Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies • Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data. Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.
Mohammad Arashi Theory of Ridge Regression Estimation with Applications Mohammad Arashi Theory of Ridge Regression Estimation with Applications Новинка

Mohammad Arashi Theory of Ridge Regression Estimation with Applications

12135.41 руб.
A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators.The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.
Yuichi Motai Data-Variant Kernel Analysis Yuichi Motai Data-Variant Kernel Analysis Новинка

Yuichi Motai Data-Variant Kernel Analysis

9749.19 руб.
Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA) Develops group kernel analysis with the distributed databases to compare speed and memory usages Explores the possibility of real-time processes by synthesizing offline and online databases Applies the assembled databases to compare cloud computing environments Examines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.
Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with JMP Pro Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with JMP Pro Новинка

Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with JMP Pro

10124.13 руб.
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.
Peter Muennig Cost-Effectiveness Analysis in Health. A Practical Approach Peter Muennig Cost-Effectiveness Analysis in Health. A Practical Approach Новинка

Peter Muennig Cost-Effectiveness Analysis in Health. A Practical Approach

7123.89 руб.
The field's bestselling reference, updated with the latest tools, data, and techniques Cost-Effectiveness Analysis in Health is a practical introduction to the tools, methods, and procedures used worldwide to perform cost-effective research. Covering every aspect of a complete cost-effectiveness analysis, this book shows you how to find which data you need, where to find it, how to analyze it, and how to prepare a high-quality report for publication. Designed for the classroom or the individual learner, the material is presented in simple and accessible language for those who lack a biostatistics or epidemiology background, and each chapter includes real-world examples and «tips and tricks» that highlight key information. Exercises throughout allow you to test your understanding with practical application, and the companion website features downloadable data sets for students, as well as lecture slides and a test bank for instructors. This new third edition contains new discussion on meta-analysis and advanced modeling techniques, a long worked example using visual modeling software TreeAge Pro, and updated recommendations from the U.S. Public Health Service's Panel on Cost-Effectiveness in Health and Medicine. Cost-effectiveness analysis is used to evaluate medical interventions worldwide, in both developed and developing countries. This book provides process-specific instruction in a concise, structured format to give you a robust working knowledge of common methods and techniques. Develop a thoroughly fleshed-out research project Work accurately with costs, probabilities, and models Calculate life expectancy and quality-adjusted life years Prepare your study and your data for publication Comprehensive analysis skills are essential for students seeking careers in public health, medicine, biomedical research, health economics, health policy, and more. Cost-Effectiveness Analysis in Health walks you through the process from a real-world perspective to help you build a skillset that's immediately applicable in the field.
Chihiro Hirotsu Advanced Analysis of Variance Chihiro Hirotsu Advanced Analysis of Variance Новинка

Chihiro Hirotsu Advanced Analysis of Variance

9373.54 руб.
Introducing a revolutionary new model for the statistical analysis of experimental data In this important book, internationally acclaimed statistician, Chihiro Hirotsu, goes beyond classical analysis of variance (ANOVA) model to offer a unified theory and advanced techniques for the statistical analysis of experimental data. Dr. Hirotsu introduces the groundbreaking concept of advanced analysis of variance (AANOVA) and explains how the AANOVA approach exceeds the limitations of ANOVA methods to allow for global reasoning utilizing special methods of simultaneous inference leading to individual conclusions. Focusing on normal, binomial, and categorical data, Dr. Hirotsu explores ANOVA theory and practice and reviews current developments in the field. He then introduces three new advanced approaches, namely: testing for equivalence and non-inferiority; simultaneous testing for directional (monotonic or restricted) alternatives and change-point hypotheses; and analyses emerging from categorical data. Using real-world examples, he shows how these three recognizable families of problems have important applications in most practical activities involving experimental data in an array of research areas, including bioequivalence, clinical trials, industrial experiments, pharmaco-statistics, and quality control, to name just a few. • Written in an expository style which will encourage readers to explore applications for AANOVA techniques in their own research • Focuses on dealing with real data, providing real-world examples drawn from the fields of statistical quality control, clinical trials, and drug testing • Describes advanced methods developed and refined by the author over the course of his long career as research engineer and statistician • Introduces advanced technologies for AANOVA data analysis that build upon the basic ANOVA principles and practices Introducing a breakthrough approach to statistical analysis which overcomes the limitations of the ANOVA model, Advanced Analysis of Variance is an indispensable resource for researchers and practitioners working in fields within which the statistical analysis of experimental data is a crucial research component. Chihiro Hirotsu is a Senior Researcher at the Collaborative Research Center, Meisei University, and Professor Emeritus at the University of Tokyo. He is a fellow of the American Statistical Association, an elected member of the International Statistical Institute, and he has been awarded the Japan Statistical Society Prize (2005) and the Ouchi Prize (2006). His work has been published in Biometrika, Biometrics, and Computational Statistics & Data Analysis, among other premier research journals.
Stéphane Tufféry Data Mining and Statistics for Decision Making Stéphane Tufféry Data Mining and Statistics for Decision Making Новинка

Stéphane Tufféry Data Mining and Statistics for Decision Making

8008.59 руб.
Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.
A. K. Md. Ehsanes Saleh An Introduction to Probability and Statistics A. K. Md. Ehsanes Saleh An Introduction to Probability and Statistics Новинка

A. K. Md. Ehsanes Saleh An Introduction to Probability and Statistics

10124.13 руб.
A well-balanced introduction to probability theory and mathematical statistics Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics. An Introduction to Probability and Statistics, Third Edition includes: A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics Additional topical coverage on bootstrapping, estimation procedures, and resampling Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks Numerous figures to further illustrate examples and proofs throughout An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.
Stig Pedersen-Bjergaard Introduction to Pharmaceutical Chemical Analysis Stig Pedersen-Bjergaard Introduction to Pharmaceutical Chemical Analysis Новинка

Stig Pedersen-Bjergaard Introduction to Pharmaceutical Chemical Analysis

16047.78 руб.
This textbook is the first to present a systematic introduction to chemical analysis of pharmaceutical raw materials, finished pharmaceutical products, and of drugs in biological fluids, which are carried out in pharmaceutical laboratories worldwide. In addition, this textbook teaches the fundamentals of all the major analytical techniques used in the pharmaceutical laboratory, and teaches the international pharmacopoeias and guidelines of importance for the field. It is primarily intended for the pharmacy student, to teach the requirements in “analytical chemistry” for the 5 years pharmacy curriculum, but the textbook is also intended for analytical chemists moving into the field of pharmaceutical analysis. Addresses the basic concepts, then establishes the foundations for the common analytical methods that are currently used in the quantitative and qualitative chemical analysis of pharmaceutical drugs Provides an understanding of common analytical techniques used in all areas of pharmaceutical development Suitable for a foundation course in chemical and pharmaceutical sciences Aimed at undergraduate students of degrees in Pharmaceutical Science/Chemistry Analytical Science/Chemistry, Forensic analysis Includes many illustrative examples
G. Antille D-optimal Design for Polynomial Regression: Choice of Degree and Robustness G. Antille D-optimal Design for Polynomial Regression: Choice of Degree and Robustness Новинка

G. Antille D-optimal Design for Polynomial Regression: Choice of Degree and Robustness

79.9 руб.
In this paper we show that for the D-optimal design, departures from the design are much less important than a departure from the model. As a consequence, we propose, based on D-optimality, a rule for choosing the regression degree. We also study different types of departures from the model to define a new class of D-optimal designs, which is robust and more efficient than the uniform one.
Eubank Randall Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators Eubank Randall Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators Новинка

Eubank Randall Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators

7775.22 руб.
Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA). The self–contained treatment of selected topics of functional analysis and operator theory includes reproducing kernel Hilbert spaces, singular value decomposition of compact operators on Hilbert spaces and perturbation theory for both self–adjoint and non self–adjoint operators. The probabilistic foundation for FDA is described from the perspective of random elements in Hilbert spaces as well as from the viewpoint of continuous time stochastic processes. Nonparametric estimation approaches including kernel and regularized smoothing are also introduced. These tools are then used to investigate the properties of estimators for the mean element, covariance operators, principal components, regression function and canonical correlations. A general treatment of canonical correlations in Hilbert spaces naturally leads to FDA formulations of factor analysis, regression, MANOVA and discriminant analysis. This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course.
Barbara Stuart H. Forensic Analytical Techniques Barbara Stuart H. Forensic Analytical Techniques Новинка

Barbara Stuart H. Forensic Analytical Techniques

14248.49 руб.
The book will be an open learning / distance learning text in the Analytical Techniques for the Sciences (AnTS) covering analytical techniques used in forensic science. No prior knowledge of the analytical techniques will be required by the reader. An introductory chapter will provide an overview of the science of the materials used as forensic evidence. Each of the following chapters will describe the techniques used in forensic analysis. The theory, instrumentation and sampling techniques will be explained and examples of the application of each technique to particular forensic samples will be provided. The reader will be able to assess their understanding with the use of regular self assessment questions and discussion questions throughout the book. The user of the book will be able to apply their understanding to the application of specific techniques to particular analyses encountered in their professional life.
Jerald Pinto E. Quantitative Investment Analysis Jerald Pinto E. Quantitative Investment Analysis Новинка

Jerald Pinto E. Quantitative Investment Analysis

6482.59 руб.
Your complete guide to quantitative analysis in the investment industry Quantitative Investment Analysis, Third Edition is a newly revised and updated text that presents you with a blend of theory and practice materials to guide you through the use of statistics within the context of finance and investment. With equal focus on theoretical concepts and their practical applications, this approachable resource offers features, such as learning outcome statements, that are targeted at helping you understand, retain, and apply the information you have learned. Throughout the text's chapters, you explore a wide range of topics, such as the time value of money, discounted cash flow applications, common probability distributions, sampling and estimation, hypothesis testing, and correlation and regression. Applying quantitative analysis to the investment process is an important task for investment pros and students. A reference that provides even subject matter treatment, consistent mathematical notation, and continuity in topic coverage will make the learning process easier—and will bolster your success. Explore the materials you need to apply quantitative analysis to finance and investment data—even if you have no previous knowledge of this subject area Access updated content that offers insight into the latest topics relevant to the field Consider a wide range of subject areas within the text, including chapters on multiple regression, issues in regression analysis, time-series analysis, and portfolio concepts Leverage supplemental materials, including the companion Workbook and Instructor's Manual, sold separately Quantitative Investment Analysis, Third Edition is a fundamental resource that covers the wide range of quantitative methods you need to know in order to apply quantitative analysis to the investment process.
Xiao-Hua Zhou Applied Missing Data Analysis in the Health Sciences Xiao-Hua Zhou Applied Missing Data Analysis in the Health Sciences Новинка

Xiao-Hua Zhou Applied Missing Data Analysis in the Health Sciences

8248.71 руб.
A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various modern statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference methods and the field of diagnostic medicine. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into traditional techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book’s subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features: Multiple data sets that can be replicated using the SAS®, Stata®, R, and WinBUGS software packages Numerous examples of case studies in the field of biostatistics to illustrate real-world scenarios and demonstrate applications of discussed methodologies Detailed appendices to guide readers through the use of the presented data in various software environments Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.

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Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.
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