739,99 zł

Applied Linear Regression

Książki Obcojęzyczne>Angielskie>Mathematics & science>Mathematics>Applied mathematicsKsiążki Obcojęzyczne>Angielskie>Mathematics & s...

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664,27 zł

Applied Regression Analysis Springer, Berlin

Książki / Literatura obcojęzyczna

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. "Applied Regression Analysis" is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge be tween a two-semester introduction to statistical methods and a thoeretical linear models course. Applied Regression Analysis emphasizes the concepts and the analysis of data sets. It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models are also discussed. Detailed case studies and exercises based on real data sets are used to reinforce the concepts. The data sets used in the book are available on the Internet.

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816,02 zł

Logistic Regression SAGE Publications Inc

Książki / Literatura obcojęzyczna

"Logistic Regression" is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally. The book begins by showing how logistic regression combines aspects of multiple linear regression and log linear analysis to overcome problems both techniques have with the analysis of dichotomous dependent variables with continuous predictors. The logistic regression model is then examined in detail, including how to evaluate the overall model and how to evaluate the impact of the different predictors in the model for different types of research questions. Unique to this book is the extensive consideration qualitative (prediction tables) as well as quantitative indices of how well the model predicts the dependent variable.The book then examines what can go wrong with the model and how to detect and correct it; the use of logistic regression in path analysis; nominal and ordinal dependent variables; modifications to the logistic regression model when the cases are not completely independent of one another; the use of logistic regression models for longitudinal data with few and with many repeated measurements; and alternatives to logistic regression. In each chapter, the basic model is explained and illustrated with applied examples, with a focus on translating from the research problem to the implementation of the model, then interpreting the results back to English. While not dependent on any one software package, limitations to existing software packages, and ways of getting around those limitations, are examined. The book brings together material on logistic regression that is often covered in passing or in limited detail in treatments of other topics such as event history analysis or multilevel analysis, and includes material not elsewhere available on the use of logistic regression with path analysis, linear panel models, and multilevel change models.Mathematical notation is kept to a minimum, allowing readers with more limited backgrounds in statistics to follow the presentation, but the book includes advanced topics that will be of interest to more statistically sophisticated readers as well.

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679,95 zł

Statistical Regression Modeling with R Springer Nature Switzerland AG

Książki / Literatura obcojęzyczna

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.

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832,00 zł

Data Analysis Using Regression and Multilevel/Hierarchical Models Cambridge University Press

Książki / Literatura obcojęzyczna

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

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334,85 zł

Data Analysis Using Regression and Multilevel/Hierarchical Models Cambridge University Press

Książki / Literatura obcojęzyczna

For the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

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685,38 zł

Applied Medical Statistics John Wiley and Sons Ltd

Książki / Literatura obcojęzyczna

APPLIED MEDICAL STATISTICSAn up-to-date exploration of foundational concepts in statistics and probability for medical students and researchersMedical journals and researchers are increasingly recognizing the need for improved statistical rigor in medical science. In Applied Medical Statistics, renowned statistician and researcher Dr. Jingmei Jiang delivers a clear, coherent, and accessible introduction to basic statistical concepts, ideal for medical students and medical research practitioners. The book will help readers master foundational concepts in statistical analysis and assist in the development of a critical understanding of the basic rationale of statistical analysis techniques.The distinguished author presents information without assuming the reader has a background in specialized mathematics, statistics, or probability. All of the described methods are illustrated with up-to-date examples based on real-world medical research, supplemented by exercises and case discussions to help solidify the concepts and give readers an opportunity to critically evaluate different research scenarios.Readers will also benefit from the inclusion of:* A thorough introduction to basic concepts in statistics, including foundational terms and definitions, location and spread of data distributions, population parameters estimation, and statistical hypothesis tests* Explorations of commonly used statistical methods, including t-tests, analysis of variance, and linear regression* Discussions of advanced analysis topics, including multiple linear regression and correlation, logistic regression, and survival analysis* Substantive exercises and case discussions at the end of each chapterPerfect for postgraduate medical students, clinicians, and medical and biomedical researchers, Applied Medical Statistics will also earn a place on the shelf of any researcher with an interest in biostatistics or applying statistical methods to their own field of research.

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512,72 zł

Applied Econometrics with R Springer-Verlag New York Inc.

Książki / Literatura obcojęzyczna

This book provides an introduction to the R system for users with a background in economics. It covers a variety of regression models (beginning with the classical linear regression model estimated by ordinary least quares,) regression diagnostics and robustness issues, the nonlinear models of microeconomics (Logit, Probit, Tobit, and further models), time series and time series econometrics (including unit roots and cointegration).

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323,99 zł

Applied Survival Analysis Using R Springer International Publishing AG

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Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. § Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics. §

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578,15 zł

Applied Multivariate Statistics with R Springer, Berlin

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Now in its second edition, this book brings multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source shareware program R, Dr. Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays; linear algebra; univariate, bivariate and multivariate normal distributions; factor methods; linear regression; discrimination and classification; clustering; time series models; and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Each chapter includes exercises, real data sets, and R implementations. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary.New to this edition are chapters devoted to longitudinal studies and the clustering of large data. This book is an excellent resources for students of multivariate statistics, as well as practitioners in the health and life sciences who are looking to integrate statistics into their work.

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679,95 zł

Applied Statistical Learning Springer, Berlin

Książki / Literatura obcojęzyczna

This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata. After introductory material on statistical learning concepts and practical aspects, each further chapter is devoted to a statistical learning algorithm or a group of related techniques. In particular, the book presents logistic regression, regularized linear models such as the Lasso, nearest neighbors, the Naive Bayes classifier, classification trees, random forests, boosting, support vector machines, feature engineering, neural networks, and stacking. It also explains how to construct n-gram variables from text data. Examples, conceptual exercises and exercises using software are featured throughout, together with case studies in Stata, mostly from the social sciences; true to the book's goal to facilitate the use of modern methods of data science in the field. Although mainly intended for upper undergraduate and graduate students in the social sciences, given its applied nature, the book will equally appeal to readers from other disciplines, including the health sciences, statistics, engineering and computer science.

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227,21 zł

Advanced Data Analysis in Neuroscience Springer International Publishing AG

Książki / Literatura obcojęzyczna

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanatory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered."Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function."Henry D. I. AbarbanelPhysics and Scripps Institution of Oceanography, University of California, San Diego"This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience. The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. "Bruno B. Averbeck

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507,10 zł

Market Risk Analysis - Practical Financial Econometrics, Volume II +CD John Wiley & Sons Inc

Książki / Literatura obcojęzyczna

Written by leading market risk academic, Professor Carol Alexander, Practical Financial Econometrics forms part two of the Market Risk Analysis four volume set. It introduces the econometric techniques that are commonly applied to finance with a critical and selective exposition, emphasising the areas of econometrics, such as GARCH, cointegration and copulas that are required for resolving problems in market risk analysis. The book covers material for a one-semester graduate course in applied financial econometrics in a very pedagogical fashion as each time a concept is introduced an empirical example is given, and whenever possible this is illustrated with an Excel spreadsheet. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM .Empirical examples and case studies specific to this volume include:* Factor analysis with orthogonal regressions and using principal component factors;* Estimation of symmetric and asymmetric, normal and Student t GARCH and E-GARCH parameters;* Normal, Student t, Gumbel, Clayton, normal mixture copula densities, and simulations from these copulas with application to VaR and portfolio optimization;* Principal component analysis of yield curves with applications to portfolio immunization and asset/liability management;* Simulation of normal mixture and Markov switching GARCH returns;* Cointegration based index tracking and pairs trading, with error correction and impulse response modelling;* Markov switching regression models (Eviews code);* GARCH term structure forecasting with volatility targeting;* Non-linear quantile regressions with applications to hedging.

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225,00 zł

Beginning Mathematica and Wolfram for Data Science APress

Książki / Literatura obcojęzyczna

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You'll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You'll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you'll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You'll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists new to using Wolfram and Mathematica as a language/tool to program in. Programmers should have some prior programming experience, but can be new to the Wolfram language.

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3746,71 zł

Categorical Data Analysis SAGE Publications Ltd

Książki / Literatura obcojęzyczna

These four volumes provide a collection of key publications on categorical data analysis, carefully put together so that the reader can easily navigate, understand and put in context the major concepts and methods of analysing categorical data. The major work opens with a series of papers that address general issues in CDA, and progresses with publications which follow a logical movement from the statistics for analysing a single categorical variable, to those for studying the relationships between two and more categorical variables, and to categorical variables in some of more advanced methods, such as latent class analysis. Edited and introduced by a leading voice in the field, this collection helpfully includes both theoretical and applied items on its theme, in order to help the reader understand the methods and use them in empirical research. Features: Volume 1: Basic Concepts and Principles; Volume 2: Statistical Methods for Analysing Associations; Volume 3: Log-Linear and Logistic Regression Models; and Volume 4: Advanced and Graphical Statistical Methods.

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