Articles by "Regression Analysis"

Showing posts with label Regression Analysis. Show all posts

Roger Koenker, Victor Chernozhukov, Xuming He, Limin Peng ... 483 pages - Publisher: Chapman and Hall/CRC; (October, 2017) ... Language: English - AmazonSIN: B076DG4VR4.

Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments.

The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.

Sanford Weisberg ... 368 pages - Publisher: Wiley; 4th edition (December, 2013) ... Language: English - ISBN-10: 9781118386088 - ISBN-13: 978-1118386088.

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.

Timothy Z. Keith ... 654 pages - Publisher: Routledge; 3rd edition (January, 2019) ... Language: English - ISBN-10: 1138061441 - ISBN-13: 978-1138061446.

Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: • Covers both MR and SEM, while explaining their relevance to one another • Includes path analysis, confirmatory factor analysis, and latent growth modeling • Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises • Extensive use of figures and tables providing examples and illustrating key concepts and techniques.

Soumya D. Mohanty ... 136 pages - Publisher: Chapman and Hall/CRC; (December, 2018) ... Language: English - ASIN: B07LCWSVMD by Amazon.

A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis.

Features: Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory + Focuses on methodology and results rather than formal proofs + Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) + Uses concrete and realistic data analysis examples to guide the reader + Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges.

Charles M. Judd, Gary H. McClelland, Carey S. Ryan ... 378 pages - Publisher: Routledge; 3rd edition (May, 2017) ... Language: English - ISBN-10: 1138819832 - ISBN-13: 978-1138819832.

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach.

Highlights of the third edition include: A new chapter on logistic regression; Expanded treatment of mixed models for data with multiple random factors; Updated examples; An enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.

Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros ... 218 pages - Publisher: Springer; (April, 2018) ... Language: English - ASIN: B07C3LK4K3 by Amazon.

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.

Mark H. Holmes ... 497 pages - Publisher: Springer; (May, 2016) ... Language: English - ISBN-10: 331930254X - ISBN-13: 978-3319302546

This textbook provides and introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. The MATLAB codes used to produce most of the figures and data tables in the text are available on the author’s website and SpringerLink.

Christian Ritz, Signe Marie Jensen, Daniel Gerhard, Jens Carl Streibig ... 226 pages - Publisher: Chapman and Hall/CRC; (July, 2019) ... Language: English - ISBN-10: 1138034312 - ISBN-13: 978-1138034310

Nowadays the term dose-response is used in many different contexts and many different scientific disciplines including agriculture, biochemistry, chemistry, environmental sciences, genetics, pharmacology, plant sciences, toxicology, and zoology. In the 1940 and 1950s, dose-response analysis was intimately linked to evaluation of toxicity in terms of binary responses, such as immobility and mortality, with a limited number of doses of a toxic compound being compared to a control group (dose 0). Later, dose-response analysis has been extended to other types of data and to more complex experimental designs. Moreover, estimation of model parameters has undergone a dramatic change, from struggling with cumbersome manual operations and transformations with pen and paper to rapid calculations on any laptop. Advances in statistical software have fueled this development.

Key Features: Provides a practical and comprehensive overview of dose-response analysis. + Includes numerous real data examples to illustrate the methodology. + R code is integrated into the text to give guidance on applying the methods. + Written with minimal mathematics to be suitable for practitioners. + Includes code and datasets on the book’s GitHub: https://github.com/DoseResponse. This book focuses on estimation and interpretation of entirely parametric nonlinear dose-response models using the powerful statistical environment R. Specifically, this book introduces dose-response analysis of continuous, binomial, count, multinomial, and event-time dose-response data. The statistical models used are partly special cases, partly extensions of nonlinear regression models, generalized linear and nonlinear regression models, and nonlinear mixed-effects models (for hierarchical dose-response data). Both simple and complex dose-response experiments will be analyzed.

Samprit Chatterjee, Jeffrey S. Simonoff ... 252 pages - Publisher: Wiley; (December, 2012) ... Language: English - ISBN-10: 0470887168 - ISBN-13: 978-0470887165

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.

Daniel J. Denis ... 224 pages - Publisher: Wiley; (September, 2018) ... Language: English - ISBN-10: 9781119465812 - ISBN-13: 978-1119465812

This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance – Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non-Parametric Tests. This helpful resource allows readers to: Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts. Make use of computational tools used by data analysis professionals. Focus on real-world application to apply concepts from the book to actual research.

Conrad Carlberg ... 568 pages - Publisher: Que Publishing; (December, 2017) ... Language: English - ISBN-10: 0789759055 - ISBN-13: 978-0789759054

Nationally recognized Excel expert Conrad Carlberg shows you how to use Excel 2016 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples and downloadable workbooks, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features. Along the way, he clarifies confusing statistical terminology and helps you avoid common mistakes. You’ll learn how to use correlation and regression, analyze variance and covariance, and test statistical hypotheses using the normal, binomial, t, and F distributions. To help you make accurate inferences based on samples from a population, Carlberg offers insightful coverage of crucial topics ranging from experimental design to the statistical power of F tests. Updated for Excel 2016, this guide covers both modern consistency functions and legacy compatibility functions. Becoming an expert with Excel statistics has never been easier! In this book, you’ll find crystal-clear instructions, insider insights, and complete step-by-step guidance.

Artificial Neural Networks: Understand The Basic Concepts [Size: 285 MB] ... Artificial Intelligence is becoming progressively more relevant in today’s world. The rise of Artificial intelligence has the potential to transform our future more than any other technology. By using the power of algorithms, you can develop applications which intelligently interact with the world around you, from building intelligent recommender systems to creating self-driving cars, robots and chatbots. Neural networks are a key element of artificial intelligence.

Neural networks are one of the most fascinating machine learning models and are used to solve wide range of problems in different areas of artificial intelligence and machine learning. Yet too few really understand how neural networks actually work. This course will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. The purpose of this course is to make neural networks accessible to as many students as possible.

In this course I’m going to explain the key aspects of neural networks and provide you with a foundation to get started with advanced topics. You will build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. You’ll understand how to solve complex computational problems efficiently.

Practical Statistics For Data And Business Analysis [Size: 6.34 GB] ... This course material is prepared from highly experienced engineers worked in a leader companies like Microsoft , Facebook and Google. After hard working from five months ago we created +270 Lectures/Articles to cover everything related to practical statistics. In no time with simple and easy way you will learn and love statistics. We stress in this course to make it very spontaneous to make all students love statistics .

John Fox, Sanford Weisberg ... 608 pages - Publisher: SAGE Publications; 3rd edition (October, 2018) ... Language: English - ISBN-10: 1544336470 - ISBN-13: 978-1544336473 ...

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the carand effects packages for R for this new edition, and include coverage of RStudio and R Markdown.

Jacek Welc, Pedro J. R. Esquerdo ... 286 pages - Publisher: Springer; (December, 2017) ... Language: English - ASIN: B078Q1RQDV by Amazon...

This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression. The authors discuss commonly applied econometric procedures, which are useful in building regression models for economic forecasting and supporting business decisions. A significant part of the book is devoted to traps and pitfalls in implementing regression analysis in real-world scenarios. The book consists of nine chapters, the final two of which are fully devoted to case studies. Today's business environment is characterised by a huge amount of economic data. Making successful business decisions under such data-abundant conditions requires objective analytical tools, which can help to identify and quantify multiple relationships between dozens of economic variables. Single-equation regression analysis, which is discussed in this book, is one such tool. The book offers a valuable guide and is relevant in various areas of economic and business analysis, including marketing, financial and operational management.

Rob J. Hyndman, George Athanasopoulos ... 382 pages - Publisher: OTexts; 2nd edition (May, 2018) ... Language: English - ISBN-10: 0987507117 - ISBN-13: 978-0987507112 ...

Forecasting is required in many situations. Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience. In this second edition, all chapters have been updated to cover the latest research and forecasting methods. Three new chapters have been added on dynamic regression forecasting, hierarchical forecasting and practical forecasting issues.

Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway ... 324 pages - Publisher: Chapman and Hall/CRC; Language: English - ISBN-10: 1498727255 - ISBN-13: 978-1498727259 ...

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download. The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work.

Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining ... 672 pages - Publisher: Wiley; 5th edition (April, 2012) ... Language: English - ISBN-10: 9780470542811 - ISBN-13: 978-0470542811 ... 

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.

Luca Massaron ... 312 pages - Publisher: Packt Publishing; (February, 2016) ... Language: English - ISBN-10: 1785286315 - ISBN-13: 978-1785286315 ...

Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.

Andrew Gelman, Jennifer Hill ... 648 pages - Publisher: Cambridge Univ. Press; (December, 2006) ... Language: English - ISBN-10: 052168689X - ISBN-13: 978-0521686891 ...

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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