# Search Results for "an-introduction-to-statistics"

## Introduction to Statistics

*Fundamental Concepts and Procedures of Data Analysis*

**Author**: Howard M. Reid**Publisher:**SAGE Publications**ISBN:**1483324281**Category:**Social Science**Page:**632**View:**3765

Using a truly accessible and reader-friendly approach, Introduction to Statistics: Fundamental Concepts and Procedures of Data Analysis, by Howard M. Reid, redefines the way statistics can be taught and learned. Unlike other books that merely focus on procedures, Reid’s approach balances development of critical thinking skills with application of those skills to contemporary statistical analysis. He goes beyond simply presenting techniques by focusing on the key concepts readers need to master in order to ensure their long-term success. Indeed, this exciting new book offers the perfect foundation upon which readers can build as their studies and careers progress to more advanced forms of statistics. Keeping computational challenges to a minimum, Reid shows readers not only how to conduct a variety of commonly used statistical procedures, but also when each procedure should be utilized and how they are related. Following a review of descriptive statistics, he begins his discussion of inferential statistics with a two-chapter examination of the Chi Square test to introduce students to hypothesis testing, the importance of determining effect size, and the need for post hoc tests. When more complex procedures related to interval/ratio data are covered, students already have a solid understanding of the foundational concepts involved. Exploring challenging topics in an engaging and easy-to-follow manner, Reid builds concepts logically and supports learning through robust pedagogical tools, the use of SPSS, numerous examples, historical quotations, insightful questions, and helpful progress checks.

## An Introduction to Statistics

*An Active Learning Approach*

**Author**: Kieth A. Carlson,Jennifer R. Winquist**Publisher:**SAGE Publications**ISBN:**1483378756**Category:**Psychology**Page:**656**View:**7100

An Introduction to Statistics: An Active Learning Approach, Second Edition by Kieth A. Carlson and Jennifer R. Winquist takes a unique, active approach to teaching and learning introductory statistics that allows students to discover and correct their misunderstandings as chapters progress rather than at their conclusion. Empirically-developed, self-correcting activities reinforce and expand on fundamental concepts, targeting and holding students’ attention. Based on contemporary memory research, this learner-centered approach leads to better long-term retention through active engagement while generating explanations. Along with carefully placed reading questions, this edition includes learning objectives, realistic research scenarios, practice problems, self-test questions, problem sets, and practice tests to help students become more confident in their ability to perform statistics.

## An Introduction to Statistical Learning

*with Applications in R*

**Author**: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani**Publisher:**Springer Science & Business Media**ISBN:**1461471389**Category:**Mathematics**Page:**426**View:**3352

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 Introduction to Statistics with Python

*With Applications in the Life Sciences*

**Author**: Thomas Haslwanter**Publisher:**Springer**ISBN:**3319283162**Category:**Computers**Page:**278**View:**6077

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.

## An Introduction to Statistical Computing

*A Simulation-based Approach*

**Author**: Jochen Voss**Publisher:**John Wiley & Sons**ISBN:**1118728025**Category:**Mathematics**Page:**400**View:**3230

A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques An Introduction to Statistical Computing: Fully covers the traditional topics of statistical computing. Discusses both practical aspects and the theoretical background. Includes a chapter about continuous-time models. Illustrates all methods using examples and exercises. Provides answers to the exercises (using the statistical computing environment R); the corresponding source code is available online. Includes an introduction to programming in R. This book is mostly self-contained; the only prerequisites are basic knowledge of probability up to the law of large numbers. Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course

## An Introduction to Statistical Thermodynamics

**Author**: Terrell L. Hill**Publisher:**Courier Corporation**ISBN:**0486130908**Category:**Science**Page:**544**View:**6496

Four-part treatment covers principles of quantum statistical mechanics, systems composed of independent molecules or other independent subsystems, and systems of interacting molecules, concluding with a consideration of quantum statistics.

## An Introduction to Statistical Concepts

*Third Edition*

**Author**: Debbie L Hahs-Vaughn,Richard G Lomax**Publisher:**Routledge**ISBN:**1136490124**Category:**Psychology**Page:**840**View:**867

This comprehensive, flexible text is used in both one- and two-semester courses to review introductory through intermediate statistics. Instructors select the topics that are most appropriate for their course. Its conceptual approach helps students more easily understand the concepts and interpret SPSS and research results. Key concepts are simply stated and occasionally reintroduced and related to one another for reinforcement. Numerous examples demonstrate their relevance. This edition features more explanation to increase understanding of the concepts. Only crucial equations are included. In addition to updating throughout, the new edition features: New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award. A new chapter on logistic regression models for today's more complex methodologies. More on computing confidence intervals and conducting power analyses using G*Power. Many more SPSS screenshots to assist with understanding how to navigate SPSS and annotated SPSS output to assist in the interpretation of results. Extended sections on how to write-up statistical results in APA format. New learning tools including chapter-opening vignettes, outlines, and a list of key concepts, many more examples, tables, and figures, boxes, and chapter summaries. More tables of assumptions and the effects of their violation including how to test them in SPSS. 33% new conceptual, computational, and all new interpretative problems. A website that features PowerPoint slides, answers to the even-numbered problems, and test items for instructors, and for students the chapter outlines, key concepts, and datasets that can be used in SPSS and other packages, and more. Each chapter begins with an outline, a list of key concepts, and a vignette related to those concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides instructions for how to run SPSS, including annotated output, and tips to develop an APA style write-up. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. 'Stop and Think' boxes provide helpful tips for better understanding the concepts. Each chapter includes computational, conceptual, and interpretive problems. The data sets used in the examples and problems are provided on the web. Answers to the odd-numbered problems are given in the book. The first five chapters review descriptive statistics including ways of representing data graphically, statistical measures, the normal distribution, and probability and sampling. The remainder of the text covers inferential statistics involving means, proportions, variances, and correlations, basic and advanced analysis of variance and regression models. Topics not dealt with in other texts such as robust methods, multiple comparison and nonparametric procedures, and advanced ANOVA and multiple and logistic regression models are also reviewed. Intended for one- or two-semester courses in statistics taught in education and/or the behavioral sciences at the graduate and/or advanced undergraduate level, knowledge of statistics is not a prerequisite. A rudimentary knowledge of algebra is required.

## An Introduction to Statistics using Microsoft Excel 2nd Edition

*An Introduction to Statistics using Microsoft Excel 2nd Edition*

**Author**: Dan Remenyi,George Onofrei,Joseph English**Publisher:**Academic Conferences and publishing limited**ISBN:**1910810169**Category:**Business & Economics**Page:**234**View:**937

The handling of numbers in arithmetic and the progression into the more abstract field of mathematics and statistics is generally approached poorly in our education system. The inadequacy is not necessarily in the teaching techniques or the books and other text used but rather in the attitude towards these subjects. These subjects are seen as something which has to be taught because it is part of a preordained curriculum rather than a set of tools which are available to help people live a fuller, more productive and more interesting life. It is so enlightening when one hears people say, "I thought that when I left school I was leaving all the maths stuff behind me!" or "I was bored witless by all those numbers and formulas [sic] that were forced down my throat." This book was written out of a frustration at seeing statistics taught through formal methods using large scale statistic software packages. It seemed to me that very little was learned by this process and quite often both the teachers and the students were in denial. It is true that the students were generally able to pick up enough knowledge to pass an examination or to complete a piece of research. But I seldom saw anything which could be regarded as deep learning and the little which had been learned did not stay for any length of time in the heads of these learners. I know people who have passed several university level courses in statistics and they can hardly recall never mind use any of what was taught to them.

## An Introduction to Statistical Inference and Its Applications with R

**Author**: Michael W. Trosset**Publisher:**CRC Press**ISBN:**9781584889489**Category:**Mathematics**Page:**496**View:**2274

Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples—not to perform entire analyses. After discussing the importance of chance in experimentation, the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples, motivating a variety of summary statistics and diagnostic techniques. The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author then explains procedures for 1- and 2-sample location problems, analysis of variance, goodness-of-fit, and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference. Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data.

## An Introduction to Statistical Methods and Data Analysis

**Author**: R. Lyman Ott,Micheal T. Longnecker**Publisher:**Cengage Learning**ISBN:**1305465520**Category:**Mathematics**Page:**1296**View:**6770

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh 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 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.

## Lectures on Biostatistics

*An Introduction to Statistics with Applications in Biology and Medicine*

**Author**: D. Colquhoun**Publisher:**David Colquhoun**ISBN:**N.A**Category:**Biomathematics**Page:**425**View:**7340

## An Introduction to Statistics

*Improving Your Grade*

**Author**: George Woodbury**Publisher:**Duxbury Pr**ISBN:**9780534389260**Category:**Business & Economics**Page:**224**View:**5323

A student solutions manual containing detailed solutions for the text's odd-numbered exercises and tips on how to solve them.

## Learning From Data

*An Introduction To Statistical Reasoning*

**Author**: Arthur Glenberg,Matthew Andrzejewski**Publisher:**Routledge**ISBN:**1136676627**Category:**Education**Page:**580**View:**6130

Learning from Data focuses on how to interpret psychological data and statistical results. The authors review the basics of statistical reasoning to helpstudents better understand relevant data that affecttheir everyday lives. Numerous examples based on current research and events are featured throughout.To facilitate learning, authors Glenberg and Andrzejewski: Devote extra attention to explaining the more difficult concepts and the logic behind them Use repetition to enhance students’ memories with multiple examples, reintroductions of the major concepts, and a focus on these concepts in the problems Employ a six-step procedure for describing all statistical tests from the simplest to the most complex Provide end-of-chapter tables to summarize the hypothesis testing procedures introduced Emphasizes how to choose the best procedure in the examples, problems and endpapers Focus on power with a separate chapter and power analyses procedures in each chapter Provide detailed explanations of factorial designs, interactions, and ANOVA to help students understand the statistics used in professional journal articles. The third edition has a user-friendly approach: Designed to be used seamlessly with Excel, all of the in-text analyses are conducted in Excel, while the book’s CD contains files for conducting analyses in Excel, as well as text files that can be analyzed in SPSS, SAS, and Systat Two large, real data sets integrated throughout illustrate important concepts Many new end-of-chapter problems (definitions, computational, and reasoning) and many more on the companion CD Online Instructor’s Resources includes answers to all the exercises in the book and multiple-choice test questions with answers Boxed media reports illustrate key concepts and their relevance to realworld issues The inclusion of effect size in all discussions of power accurately reflects the contemporary issues of power, effect size, and significance. Learning From Data, Third Edition is intended as a text for undergraduate or beginning graduate statistics courses in psychology, education, and other applied social and health sciences.

## An Introduction to Statistical Analysis of Random Arrays

**Author**: Vâčeslav Leonidovič Girko**Publisher:**VSP**ISBN:**9789067642934**Category:**Mathematics**Page:**673**View:**2819

This book contains the results of 30 years of investigation by the author into the creation of a new theory on statistical analysis of observations, based on the principle of random arrays of random vectors and matrices of increasing dimensions. It describes limit phenomena of sequences of random observations, which occupy a central place in the theory of random matrices. This is the first book to explore statistical analysis of random arrays and provides the necessary tools for such analysis. This book is a natural generalization of multidimensional statistical analysis and aims to provide its readers with new, improved estimators of this analysis. The book consists of 14 chapters and opens with the theory of sample random matrices of fixed dimension, which allows to envelop not only the problems of multidimensional statistical analysis, but also some important problems of mechanics, physics and economics. The second chapter deals with all 50 known canonical equations of the new statistical analysis, which form the basis for finding new and improved statistical estimators. Chapters 3-5 contain detailed proof of the three main laws on the theory of sample random matrices. In chapters 6-10 detailed, strong proofs of the Circular and Elliptic Laws and their generalization are given. In chapters 11-13 the convergence rates of spectral functions are given for the practical application of new estimators and important questions on random matrix physics are considered. The final chapter contains 54 new statistical estimators, which generalize the main estimators of statistical analysis.

## Understanding Business Statistics

**Author**: Ned Freed,Stacey Jones,Timothy Bergquist**Publisher:**Wiley Global Education**ISBN:**1118799143**Category:**Business & Economics**Page:**608**View:**4910

Understanding Business Statistics is a highly student-oriented business statistics product that makes statistics understandable for students with a wide variety of statistics backgrounds. The authors provide an intuitive discussion of basic statistical principles rather than a mathematically rigorous development. They use simple examples to introduce and develop concepts and procedures. For ease of reading, chapter sections are designed to ensure easy-to-follow continuity from one section to the next. This text provides students with frequent opportunities to check their understanding of topics as they move through the chapters, with exercises included at the end of most sections. In many cases, the exercises have been designed to extend chapter discussions rather than solely provide opportunities for drill and repetition. Understanding Business Statistics isÊwritten using a modular approach, allowing students to approach the subject step-by-step with very clear instructions.

## An Introduction to Statistics

**Author**: George Woodbury**Publisher:**Cengage Learning**ISBN:**1111793425**Category:**Mathematics**Page:**720**View:**3921

Many statistics texts lack well-defined connections among materials presented, as if the different topics were disjointed. In this new text, George Woodbury successfully illustrates the natural connections between probability and inferential statistics an

## An Introduction to Statistical Analysis in Research

*With Applications in the Biological and Life Sciences*

**Author**: Kathleen F. Weaver,Vanessa C. Morales,Sarah L. Dunn,Kanya Godde,Pablo F. Weaver**Publisher:**John Wiley & Sons**ISBN:**1119301106**Category:**Mathematics**Page:**616**View:**6769

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.

## An introduction to statistical sampling in auditing

**Author**: Dan M. Guy**Publisher:**John Wiley & Sons**ISBN:**N.A**Category:**Business & Economics**Page:**229**View:**2249

## An Introduction to Statistical Modeling of Extreme Values

**Author**: Stuart Coles**Publisher:**Springer Science & Business Media**ISBN:**1447136756**Category:**Mathematics**Page:**209**View:**544

Directly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society's research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.

## Biostatistics with R

*An Introduction to Statistics Through Biological Data*

**Author**: Babak Shahbaba**Publisher:**Springer Science & Business Media**ISBN:**1461413028**Category:**Medical**Page:**352**View:**919

Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis. This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text.