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Author : J. Hine
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (873 download)
Download or read book A Programmed Text in Statistics written by J. Hine and published by . This book was released on 1975 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Joan Hine
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (75 download)
Download or read book A Programmed Text in Statistics: Basic theory written by Joan Hine and published by . This book was released on 1975 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Hine
Publisher :
ISBN 13 : 9780470400418
Total Pages : 109 pages
Book Rating : 4.4/5 (4 download)
Download or read book Programmed Text in Statistics Book 2 Basic Theory written by Hine and published by . This book was released on 1975-10-01 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : J. Hine
Publisher : Springer
ISBN 13 : 9780412137303
Total Pages : 109 pages
Book Rating : 4.1/5 (373 download)
Download or read book Programmed Text in Statistics written by J. Hine and published by Springer. This book was released on 1975-09-25 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Joan Hine
Publisher : Chapman & Hall
ISBN 13 : 9780470400401
Total Pages : 95 pages
Book Rating : 4.4/5 (4 download)
Download or read book A Programmed Text in Statistics written by Joan Hine and published by Chapman & Hall. This book was released on 1975-01-01 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Gareth James
Publisher : Springer Nature
ISBN 13 : 3031387473
Total Pages : 617 pages
Book Rating : 4.0/5 (313 download)
Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2023-08-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Author : Joan Hine
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (75 download)
Download or read book Basic theory written by Joan Hine and published by . This book was released on 1975 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Daniel Navarro
Publisher : Lulu.com
ISBN 13 : 1326189727
Total Pages : 617 pages
Book Rating : 4.3/5 (261 download)
Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 395 pages
Book Rating : 4.4/5 (919 download)
Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Author : Mike X Cohen
Publisher : MIT Press
ISBN 13 : 0262019876
Total Pages : 615 pages
Book Rating : 4.2/5 (62 download)
Download or read book Analyzing Neural Time Series Data written by Mike X Cohen and published by MIT Press. This book was released on 2014-01-17 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.
Author : Openstax
Publisher :
ISBN 13 : 9788565775120
Total Pages : 914 pages
Book Rating : 4.7/5 (751 download)
Download or read book Introductory Statistics written by Openstax and published by . This book was released on 2022-03-23 with total page 914 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs. Senior Contributing Authors Barbara Illowsky, De Anza College Susan Dean, De Anza College Contributing Authors Daniel Birmajer, Nazareth College Bryan Blount, Kentucky Wesleyan College Sheri Boyd, Rollins College Matthew Einsohn, Prescott College James Helmreich, Marist College Lynette Kenyon, Collin County Community College Sheldon Lee, Viterbo University Jeff Taub, Maine Maritime Academy
Author : Mervyn G. Marasinghe
Publisher : Springer
ISBN 13 : 3319692399
Total Pages : 688 pages
Book Rating : 4.3/5 (196 download)
Download or read book Statistical Data Analysis Using SAS written by Mervyn G. Marasinghe and published by Springer. This book was released on 2018-04-12 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: • Covers SAS v9.2 and incorporates new commands • Uses SAS ODS (output delivery system) for reproduction of tables and graphics output • Presents new commands needed to produce ODS output • All chapters rewritten for clarity • New and updated examples throughout • All SAS outputs are new and updated, including graphics • More exercises and problems • Completely new chapter on analysis of nonlinear and generalized linear models • Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.
Author : Anthony Almudevar
Publisher : CRC Press
ISBN 13 : 1000488071
Total Pages : 1059 pages
Book Rating : 4.0/5 (4 download)
Download or read book Theory of Statistical Inference written by Anthony Almudevar and published by CRC Press. This book was released on 2021-12-30 with total page 1059 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or advanced undergraduate level. It presents a unified treatment of the foundational ideas of modern statistical inference, and would be suitable for a core course in a graduate program in statistics or biostatistics. The emphasis is on the application of mathematical theory to the problem of inference, leading to an optimization theory allowing the choice of those statistical methods yielding the most efficient use of data. The book shows how a small number of key concepts, such as sufficiency, invariance, stochastic ordering, decision theory and vector space algebra play a recurring and unifying role. The volume can be divided into four sections. Part I provides a review of the required distribution theory. Part II introduces the problem of statistical inference. This includes the definitions of the exponential family, invariant and Bayesian models. Basic concepts of estimation, confidence intervals and hypothesis testing are introduced here. Part III constitutes the core of the volume, presenting a formal theory of statistical inference. Beginning with decision theory, this section then covers uniformly minimum variance unbiased (UMVU) estimation, minimum risk equivariant (MRE) estimation and the Neyman-Pearson test. Finally, Part IV introduces large sample theory. This section begins with stochastic limit theorems, the δ-method, the Bahadur representation theorem for sample quantiles, large sample U-estimation, the Cramér-Rao lower bound and asymptotic efficiency. A separate chapter is then devoted to estimating equation methods. The volume ends with a detailed development of large sample hypothesis testing, based on the likelihood ratio test (LRT), Rao score test and the Wald test. Features This volume includes treatment of linear and nonlinear regression models, ANOVA models, generalized linear models (GLM) and generalized estimating equations (GEE). An introduction to decision theory (including risk, admissibility, classification, Bayes and minimax decision rules) is presented. The importance of this sometimes overlooked topic to statistical methodology is emphasized. The volume emphasizes throughout the important role that can be played by group theory and invariance in statistical inference. Nonparametric (rank-based) methods are derived by the same principles used for parametric models and are therefore presented as solutions to well-defined mathematical problems, rather than as robust heuristic alternatives to parametric methods. Each chapter ends with a set of theoretical and applied exercises integrated with the main text. Problems involving R programming are included. Appendices summarize the necessary background in analysis, matrix algebra and group theory.
Author : Peter Dalgaard
Publisher : Springer Science & Business Media
ISBN 13 : 0387790543
Total Pages : 370 pages
Book Rating : 4.3/5 (877 download)
Download or read book Introductory Statistics with R written by Peter Dalgaard and published by Springer Science & Business Media. This book was released on 2008-06-27 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.
Author : David M Lane
Publisher :
ISBN 13 : 9781687894250
Total Pages : 406 pages
Book Rating : 4.8/5 (942 download)
Download or read book Online Statistics Education written by David M Lane and published by . This book was released on 2014-12-02 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.This print edition of the public domain textbook gives the student an opportunity to own a physical copy to help enhance their educational experience. This part I features the book Front Matter, Chapters 1-10, and the full Glossary. Chapters Include:: I. Introduction, II. Graphing Distributions, III. Summarizing Distributions, IV. Describing Bivariate Data, V. Probability, VI. Research Design, VII. Normal Distributions, VIII. Advanced Graphs, IX. Sampling Distributions, and X. Estimation. Online Statistics Education: A Multimedia Course of Study (http: //onlinestatbook.com/). Project Leader: David M. Lane, Rice University.
Author : Chester Ismay
Publisher : CRC Press
ISBN 13 : 1000763463
Total Pages : 461 pages
Book Rating : 4.0/5 (7 download)
Download or read book Statistical Inference via Data Science: A ModernDive into R and the Tidyverse written by Chester Ismay and published by CRC Press. This book was released on 2019-12-23 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
Author : Shailaja Deshmukh
Publisher : Springer
ISBN 13 : 9789811590023
Total Pages : 495 pages
Book Rating : 4.5/5 (9 download)
Download or read book Asymptotic Statistical Inference written by Shailaja Deshmukh and published by Springer. This book was released on 2021-06-28 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed, the focus being on the consistent and asymptotically normal (CAN) estimators. It is shown that for the probability models belonging to an exponential family and a Cramer family, the maximum likelihood estimators of the indexing parameters are CAN. The book describes some large sample test procedures, in particular, the most frequently used likelihood ratio test procedure. Various applications of the likelihood ratio test procedure are addressed, when the underlying probability model is a multinomial distribution. These include tests for the goodness of fit and tests for contingency tables. The book also discusses a score test and Wald’s test, their relationship with the likelihood ratio test and Karl Pearson’s chi-square test. An important finding is that, while testing any hypothesis about the parameters of a multinomial distribution, a score test statistic and Karl Pearson’s chi-square test statistic are identical. Numerous illustrative examples of differing difficulty level are incorporated to clarify the concepts. For better assimilation of the notions, various exercises are included in each chapter. Solutions to almost all the exercises are given in the last chapter, to motivate students towards solving these exercises and to enable digestion of the underlying concepts. The concepts from asymptotic inference are crucial in modern statistics, but are difficult to grasp in view of their abstract nature. To overcome this difficulty, keeping up with the recent trend of using R software for statistical computations, the book uses it extensively, for illustrating the concepts, verifying the properties of estimators and carrying out various test procedures. The last section of the chapters presents R codes to reveal and visually demonstrate the hidden aspects of different concepts and procedures. Augmenting the theory with R software is a novel and a unique feature of the book. The book is designed primarily to serve as a text book for a one semester introductory course in asymptotic statistical inference, in a post-graduate program, such as Statistics, Bio-statistics or Econometrics. It will also provide sufficient background information for studying inference in stochastic processes. The book will cater to the need of a concise but clear and student-friendly book introducing, conceptually and computationally, basics of asymptotic inference.
