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An Introduction To Statistical Learning

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An Introduction to Statistical Learning

An Introduction to Statistical Learning Book
Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
Publisher : Springer Science & Business Media
Release : 2013-06-24
ISBN : 1461471389
Language : En, Es, Fr & De

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Book Description :

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

An Elementary Introduction to Statistical Learning Theory

An Elementary Introduction to Statistical Learning Theory Book
Author : Sanjeev Kulkarni,Gilbert Harman
Publisher : John Wiley & Sons
Release : 2011-06-09
ISBN : 9781118023464
Language : En, Es, Fr & De

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Book Description :

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

Machine Learning and Data Science

Machine Learning and Data Science Book
Author : Daniel D. Gutierrez
Publisher : Technics Publications
Release : 2015-11-01
ISBN : 1634620984
Language : En, Es, Fr & De

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Book Description :

A practitioner’s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field – recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.

An Introduction to Statistical Learning

An Introduction to Statistical Learning Book
Author : Peter Forrest
Publisher : Createspace Independent Publishing Platform
Release : 2017-07-04
ISBN : 9781979811040
Language : En, Es, Fr & De

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Book Description :

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Introduction to Statistical Relational Learning

Introduction to Statistical Relational Learning Book
Author : Lise Getoor,Ben Taskar
Publisher : MIT Press
Release : 2007
ISBN : 0262072882
Language : En, Es, Fr & De

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Book Description :

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.

The Elements of Statistical Learning

The Elements of Statistical Learning Book
Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
Publisher : Springer Science & Business Media
Release : 2013-11-11
ISBN : 0387216065
Language : En, Es, Fr & De

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Book Description :

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Learning From Data

Learning From Data Book
Author : Arthur Glenberg,Matthew Andrzejewski
Publisher : Routledge
Release : 2007-08-09
ISBN : 1136676627
Language : En, Es, Fr & De

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Book Description :

Learning from Data reviews the basics of statistical reasoning to help students understand psychological data that affect their lives. To facilitate learning the authors devote extra attention to explaining the difficult concepts, use repetition to enhance memory and illustrate concepts with numerous examples. A six-step procedure helps students apply all statistical tests, from simple to complex. The authors emphasize how to choose the best statistical procedure in the text, the examples and the problems. Intended for undergraduate or graduate statistics courses in psychology, education, and other applied social and health sciences.

The Nature of Statistical Learning Theory

The Nature of Statistical Learning Theory Book
Author : Vladimir Vapnik
Publisher : Springer Science & Business Media
Release : 1999-11-19
ISBN : 9780387987804
Language : En, Es, Fr & De

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Book Description :

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning Book
Author : Masashi Sugiyama
Publisher : Morgan Kaufmann
Release : 2015-10-31
ISBN : 0128023503
Language : En, Es, Fr & De

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Book Description :

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus. Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning. Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

An Introduction to Statistics

An Introduction to Statistics Book
Author : Kieth A. Carlson,Jennifer R. Winquist
Publisher : SAGE Publications
Release : 2013-01-09
ISBN : 148332155X
Language : En, Es, Fr & De

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Book Description :

An Introduction to Statistics is the ideal text for incorporating an active learning approach to the subject of introductory statistics. Authors Kieth A. Carlson and Jennifer R. Winquist carefully explain fundamental statistical concepts in short, easy-to-understand chapters, then use empirically developed workbook activities to both reinforce and expand on these fundamental concepts. These activities are self-correcting so students discover and correct their own misunderstandings early in the learning process. This approach enables students to be responsible for their own learning by helping them to "read with purpose. Based on contemporary memory research (e.g., the testing effect, embedded reading questions), the text is designed to actively engage students while they generate explanations, which leads to better long term retention. It also contains more traditional student aids including carefully developed learning objectives, realistic research scenarios, practice problems, and self-test questions.

Statistical Learning for Big Dependent Data

Statistical Learning for Big Dependent Data Book
Author : Daniel Peña,Ruey S. Tsay
Publisher : John Wiley & Sons
Release : 2021-03-16
ISBN : 1119417414
Language : En, Es, Fr & De

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Book Description :

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.

Statistical Learning with Sparsity

Statistical Learning with Sparsity Book
Author : Trevor Hastie,Robert Tibshirani,Martin Wainwright
Publisher : CRC Press
Release : 2015-05-07
ISBN : 1498712177
Language : En, Es, Fr & De

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Book Description :

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of l1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

An Introduction to Statistical Methods and Data Analysis

An Introduction to Statistical Methods and Data Analysis Book
Author : R. Lyman Ott,Micheal T. Longnecker
Publisher : Cengage Learning
Release : 2008-12-30
ISBN : 0495017582
Language : En, Es, Fr & De

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Book Description :

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Neural Networks and Statistical Learning

Neural Networks and Statistical Learning Book
Author : Ke-Lin Du,M. N. S. Swamy
Publisher : Springer Nature
Release : 2019-09-12
ISBN : 1447174526
Language : En, Es, Fr & De

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Book Description :

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Statistical Learning Theory

Statistical Learning Theory Book
Author : Vladimir N. Vapnik,VLADIMIR AUTOR VAPNIK
Publisher : Wiley-Interscience
Release : 1998-09-30
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Introduction: The Problem of Induction and Statistical Inference. Two Approaches to the Learning Problem. Appendix to Chapter1: Methods for Solving III-Posed Problems. Estimation of the Probability Measure and Problem of Learning. Conditions for Consistency of Empirical Risk Minimization Principle. Bounds on the Risk for Indicator Loss Functions. Appendix to Chapter 4: Lower Bounds on the Risk of the ERM Principle. Bounds on the Risk for Real-Valued Loss Functions. The Structural Risk Minimization Principle. Appendix to Chapter 6: Estimating Functions on the Basis of Indirect Measurements. Stochastic III-Posed Problems. Estimating the Values of Function at Given Points. Perceptrons and Their Generalizations. The Support Vector Method for Estimating Indicator Functions. The Support Vector Method for Estimating Real-Valued Functions. SV Machines for Pattern Recognition. SV Machines for Function Approximations, Regression Estimation, and Signal Processing. Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to Their Probabilities. Necessary and Sufficient Conditions for Uniform Convergence of Means to Their Expectations. Necessary and Sufficient Conditions for Uniform One-Sided Convergence of Means to Their Expectations.

Statistical Learning with Math and R

Statistical Learning with Math and R Book
Author : Joe Suzuki
Publisher : Springer Nature
Release : 2021-05-08
ISBN : 9811575681
Language : En, Es, Fr & De

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Book Description :

Download Statistical Learning with Math and R book written by Joe Suzuki, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

A Gentle Introduction to Statistics Using SASR Studio

A Gentle Introduction to Statistics Using SASR Studio Book
Author : Ron Cody
Publisher : SAS Institute
Release : 2019-09-27
ISBN : 1642955345
Language : En, Es, Fr & De

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Book Description :

Point and click your way to performing statistics! Many people are intimidated by learning statistics, but A Gentle Introduction to Statistics Using SAS Studio is here to help. Whether you need to perform statistical analysis for a project or, perhaps, for a course in education, psychology, sociology, economics, or any other field that requires basic statistical skills, this book teaches the fundamentals of statistics, from designing your experiment through calculating logistic regressions. Serving as an introduction to many common statistical tests and principles, it explains concepts in a non-technical way with little math and very few formulas. Once the basic statistical concepts are covered, the book then demonstrates how to use them with SAS Studio and SAS University Edition’s easy point-and-click interface. Topics included in this book are: How to install and use SAS University Edition Descriptive statistics One-sample tests T tests (for independent or paired samples) One-way analysis of variance (ANOVA) N-way ANOVA Correlation analysis Simple and multiple linear regression Binary logistic regression Categorical data, including two-way tables and chi-square Power and sample size calculations Questions are provided to test your knowledge and practice your skills.

Machine Learning

Machine Learning Book
Author : Rodrigo Fernandes de Mello,Moacir Antonelli Ponti
Publisher : Springer
Release : 2018-08-01
ISBN : 3319949896
Language : En, Es, Fr & De

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Book Description :

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.

An Introduction to Statistical Analysis in Research

An Introduction to Statistical Analysis in Research Book
Author : Kathleen F. Weaver,Vanessa C. Morales,Sarah L. Dunn,Pablo F. Weaver,Kanya Godde
Publisher : John Wiley & Sons
Release : 2017-09-05
ISBN : 1119299683
Language : En, Es, Fr & De

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Book Description :

Provides well-organized coverage of statistical analysis and applications in biology, kinesiology, and physical anthropology with comprehensive insights into the techniques and interpretations of R, SPSS®, Excel®, and Numbers® output An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences develops a conceptual foundation in statistical analysis while providing readers with opportunities to practice these skills via research-based data sets in biology, kinesiology, and physical anthropology. Readers are provided with a detailed introduction and orientation to statistical analysis as well as practical examples to ensure a thorough understanding of the concepts and methodology. In addition, the book addresses not just the statistical concepts researchers should be familiar with, but also demonstrates their relevance to real-world research questions and how to perform them using easily available software packages including R, SPSS®, Excel®, and Numbers®. Specific emphasis is on the practical application of statistics in the biological and life sciences, while enhancing reader skills in identifying the research questions and testable hypotheses, determining the appropriate experimental methodology and statistical analyses, processing data, and reporting the research outcomes. In addition, this book: • Aims to develop readers’ skills including how to report research outcomes, determine the appropriate experimental methodology and statistical analysis, and identify the needed research questions and testable hypotheses • Includes pedagogical elements throughout that enhance the overall learning experience including case studies and tutorials, all in an effort to gain full comprehension of designing an experiment, considering biases and uncontrolled variables, analyzing data, and applying the appropriate statistical application with valid justification • Fills the gap between theoretically driven, mathematically heavy texts and introductory, step-by-step type books while preparing readers with the programming skills needed to carry out basic statistical tests, build support figures, and interpret the results • Provides a companion website that features related R, SPSS, Excel, and Numbers data sets, sample PowerPoint® lecture slides, end of the chapter review questions, software video tutorials that highlight basic statistical concepts, and a student workbook and instructor manual An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences is an ideal textbook for upper-undergraduate and graduate-level courses in research methods, biostatistics, statistics, biology, kinesiology, sports science and medicine, health and physical education, medicine, and nutrition. The book is also appropriate as a reference for researchers and professionals in the fields of anthropology, sports research, sports science, and physical education. KATHLEEN F. WEAVER, PhD, is Associate Dean of Learning, Innovation, and Teaching and Professor in the Department of Biology at the University of La Verne. The author of numerous journal articles, she received her PhD in Ecology and Evolutionary Biology from the University of Colorado. VANESSA C. MORALES, BS, is Assistant Director of the Academic Success Center at the University of La Verne. SARAH L. DUNN, PhD, is Associate Professor in the Department of Kinesiology at the University of La Verne and is Director of Research and Sponsored Programs. She has authored numerous journal articles and received her PhD in Health and Exercise Science from the University of New South Wales. KANYA GODDE, PhD, is Assistant Professor in the Department of Anthropology and is Director/Chair of Institutional Review Board at the University of La Verne. The author of numerous journal articles and a member of the American Statistical Association, she received her PhD in Anthropology from the University of Tennessee. PABLO F. WEAVER, PhD, is Instructor in the Department of Biology at the University of La Verne. The author of numerous journal articles, he received his PhD in Ecology and Evolutionary Biology from the University of Colorado.

A Computational Approach to Statistical Learning

A Computational Approach to Statistical Learning Book
Author : Taylor Arnold,Michael Kane,Bryan W. Lewis
Publisher : CRC Press
Release : 2019-01-23
ISBN : 1351694758
Language : En, Es, Fr & De

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Book Description :

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.