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Author : Barbara Illowsky
Susan Dean
Publisher :
Release : 2017-12-19
Page : 906
Category : Education
ISBN 13 : 9789888407309
Description :


Introductory Statistics is designed for the one-semester, introduction to statistics course and is geared toward students majoring in fields other than math or engineering. This text assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it. The foundation of this textbook is Collaborative Statistics, by Barbara Illowsky and Susan Dean. Additional topics, examples, and ample opportunities for practice have been added to each chapter. The development choices for this textbook were made with the guidance of many faculty members who are deeply involved in teaching this course. These choices led to innovations in art, terminology, and practical applications, all with a goal of increasing relevance and accessibility for students. We strove to make the discipline meaningful, so that students can draw from it a working knowledge that will enrich their future studies and help them make sense of the world around them. Coverage and Scope Chapter 1 Sampling and Data Chapter 2 Descriptive Statistics Chapter 3 Probability Topics Chapter 4 Discrete Random Variables Chapter 5 Continuous Random Variables Chapter 6 The Normal Distribution Chapter 7 The Central Limit Theorem Chapter 8 Confidence Intervals Chapter 9 Hypothesis Testing with One Sample Chapter 10 Hypothesis Testing with Two Samples Chapter 11 The Chi-Square Distribution Chapter 12 Linear Regression and Correlation Chapter 13 F Distribution and One-Way ANOVA


Author : Darrell Huff
Publisher : W. W. Norton & Company
Release : 2010-12-07
Page : 144
Category : Mathematics
ISBN 13 : 0393070875
Description :


If you want to outsmart a crook, learn his tricks—Darrell Huff explains exactly how in the classic How to Lie with Statistics. From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff’s lively and engaging primer clarifies the basic principles of statistics and explains how they’re used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.


Author : Gordon A. Fox
Pofessor Department of Integrative Biology Gordon A Fox
Publisher : Oxford University Press, USA
Release : 2015-01-29
Page : 416
Category : Ecology
ISBN 13 : 0199672555
Description :


The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.


Author : David C. Howell
Publisher : Cengage Learning
Release : 2013-03-01
Page : 672
Category : Psychology
ISBN 13 : 9781285076911
Description :


FUNDAMENTAL STATISTICS FOR THE BEHAVIORAL SCIENCES focuses on providing the context of statistics in behavioral research, while emphasizing the importance of looking at data before jumping into a test. This practical approach provides readers with an understanding of the logic behind the statistics, so they understand why and how certain methods are used--rather than simply carry out techniques by rote. Readers move beyond number crunching to discover the meaning of statistical results and appreciate how the statistical test to be employed relates to the research questions posed by an experiment. An abundance of real data and research studies provide a real-life perspective and help you understand concepts as you learn about the analysis of data. Available with InfoTrac Student Collections http://gocengage.com/infotrac. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.


Author : David Diez
Mine Çetinkaya-Rundel
Publisher :
Release : 2019-05
Page : 422
Category :
ISBN 13 : 9781943450138
Description :



Author : Thomas Haslwanter
Publisher : Springer
Release : 2016-07-20
Page : 278
Category : Computers
ISBN 13 : 3319283162
Description :


This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.


Author : Trevor Hastie
Robert Tibshirani
Publisher : Springer Science & Business Media
Release : 2013-11-11
Page : 536
Category : Mathematics
ISBN 13 : 0387216065
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.


Author : Larry Wasserman
Publisher : Springer Science & Business Media
Release : 2013-12-11
Page : 442
Category : Mathematics
ISBN 13 : 0387217363
Description :


Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.


Author : Tilman M. Davies
Publisher : No Starch Press
Release : 2016-07-16
Page : 832
Category : Computers
ISBN 13 : 1593277792
Description :


The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.


Author : W. J. Conover
Publisher : John Wiley & Sons
Release : 1998-12-28
Page : 608
Category : Mathematics
ISBN 13 : 0471160687
Description :


This highly-regarded text serves as a quick reference book which offers clear, concise instructions on how and when to use the most popular nonparametric procedures. This edition features some procedures that have withstood the test of time and are now used by many practitioners, such as the Fisher Exact Test for two-by-two contingency tables, the Mantel-Haenszel Test for combining several contingency tables, the Kaplan-Meier estimates of the survival curve, the Jonckheere-Terpstra Test and the Page Test for ordered alternatives, and a discussion of the bootstrap method.


Author : Frederick Gravetter
Larry Wallnau
Publisher : Wadsworth Publishing Company
Release : 2008
Page : 575
Category : Psychology
ISBN 13 :
Description :


This brief version of Gravetter and Wallnau's proven best-seller offers the straightforward instruction, accuracy, built-in learning aids, and wealth of real-world examples that professors AND students have come to appreciate. The authors take time to explain statistical procedures so that students can go beyond memorizing formulas and gain a conceptual understanding of statistics. To ensure that even students with a weak background in mathematics can understand statistics, the authors skillfully by integrate applications that reinforce concepts. The authors take care to show students how having an understanding of statistical procedures will help them comprehend published findings and will lead them to become savvy consumers of information. Known for its exceptional accuracy and examples, this text also has a complete supplements package to support instructors with class preparation and testing. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.


Author : Peter Bruce
Andrew Bruce
Publisher : "O'Reilly Media, Inc."
Release : 2017-05-10
Page : 318
Category : Computers
ISBN 13 : 1491952911
Description :


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 : Antonio Curtis
Publisher : Createspace Independent Publishing Platform
Release : 2017-08-07
Page : 350
Category :
ISBN 13 : 9781974008209
Description :


This text provides a different approach to a first course in Statistics. The most essential topics are explored in depth, with detailed explanations of examples. The goal is to prepare students for upper division courses in which Statistics will be used so that they walk into those courses ready to grasp the new material. Probability is discussed minimally, solely for the purpose of enabling students to use Statistics. Students with only an algebra background and no science background can absorb and benefit from this book. The entire text can realistically be thoroughly covered in a one semester or one quarter course. After learning the material in this text, students will be able to assess a research situation, understand the correct statistical method to use for that situation, and then completely understand the real world meaning of the results obtained from using the procedure. There is variety in the way problems are worded so that students do not figure out what to do based on memorizing patterns in problem structure. "Extra information" is routinely included in problems so students need to think critically. Both by-hand and computer-based problems are presented so students gain an appreciation for how the statistical methods work, and can also interpret the output of technology-based statistical analysis. Descriptive statistics and inferential statistics are considered, including one- and two-factor ANOVA as well as the chi-square tests for Goodness of Fit and Independence. At the end of confidence interval and hypothesis test chapters there is a section containing mixtures of various types of problems so students need to figure out how to handle problems without the usual context clue of similar problems being grouped together. The text contains a balance of theory and practice, with indications of how the current material leads to upper division studies. An earnest effort is made to get students excited about Statistics, not simply to pass their Statistics class.


Author : Susan Holmes
Wolfgang Huber
Publisher : Cambridge University Press
Release : 2018-11-30
Page : 400
Category :
ISBN 13 : 1108427022
Description :


A far-reaching course in practical advanced statistics for biologists using R/Bioconductor, data exploration, and simulation.


Author : Hung T. Nguyen
Gerald S. Rogers
Publisher : Springer
Release : 2011-10-21
Page : 422
Category : Mathematics
ISBN 13 : 9781461389163
Description :


This is a text (divided into two volumes) for a two semester course in Mathematical Statistics at the Senior/Graduate level. The two main pedagogical aspects in these Volumes are: (i) the material is designed in lessons (each for a 50 minute class) with complementary exercises and home work. (ii) although the material is traditional, great care is exerted upon self-contained, rigorous and complete presentations. An elementary introduction to characteristic functions and probability measures and intergration, but not general measure theory in Volume I, allows a complete proof of some central limit theorems and a rigorous treatment of asymptotic of statistical inference. But students need to be familiar only with such things as Jacobians and eigenvalues of matrices. Volume II: Statistical Inference is designed for the second semester and contains a rigorous introduction to Mathematical Statistics, from random samples to asymptotic theory of statistical inference.


Author : Richard McElreath
Publisher : CRC Press
Release : 2018-01-03
Page : 487
Category : Mathematics
ISBN 13 : 1315362619
Description :


Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.


Author : Sarah Boslaugh
Publisher : "O'Reilly Media, Inc."
Release : 2012-11-15
Page : 569
Category : Mathematics
ISBN 13 : 1449316824
Description :


A clear and concise introduction and reference for anyone new to the subject of statistics.


Author : Anthony Atkinson
Stephen E. Fienberg
Publisher : Springer
Release : 2011-11-08
Page : 606
Category : Mathematics
ISBN 13 : 9781461385622
Description :


The International Statistical Institute was founded in 1885 and is therefore one of the world's oldest international scientific societies. The field of statistics is still expanding rapidly and possesses a rich variety of applications in many areas of human activity such as science, government, business, industry, and everyday affairs. In consequence, the celebration of the Institute's centenary in 1985 is of considerable interest not only to statisticians but also more widely to the international scientific community. As part of its centennial celebration planning the Institute decided to publish a volume of papers representing the immensely wide range of interests encompassed by statistics in its international context, viewed both from a historical and from a contemporary standpoint. We were fortunate in securing the services of Anthony Atkinson and Stephen Fienberg as Editors of this volume: they have worked hard over a period of several years to put together a most fascinating collection of papers. On behalf of the Institute it is my pleasant duty to thank them and the authors for their contributions. J. DURBIN, President International Statistical Institute Preface The papers in this volume were prepared to help celebrate the centenary of the International Statistical Institute. During the lSI's first 100 years statistics has matured, both as a scientific discipline and as a profession, in ways that the lSI's founders could not possibly have imagined.


Author :
Publisher :
Release :
Page :
Category :
ISBN 13 : 1107043166
Description :



Author : Gareth Michael James
Daniela Witten
Publisher : Springer Nature
Release : 2021
Page :
Category : Electronic books
ISBN 13 : 1071614185
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, deep learning, survival analysis, multiple testing, 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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.