Search results for: introductory-statistics-with-r

Introductory Statistics with R

Author : Peter Dalgaard
File Size : 65.70 MB
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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.

Using R for Introductory Statistics

Author : John Verzani
File Size : 45.61 MB
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The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

Learning Statistics with R

Author : Daniel Navarro
File Size : 39.88 MB
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Solutions Manual for Using R for Introductory Statistics

Author : John Verzani
File Size : 83.62 MB
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Introductory Statistics

Author : William B. Ware
File Size : 23.13 MB
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This comprehensive and uniquely organized text is aimed at undergraduate and graduate level statistics courses in education, psychology, and other social sciences. A conceptual approach, built around common issues and problems rather than statistical techniques, allows students to understand the conceptual nature of statistical procedures and to focus more on cases and examples of analysis. Wherever possible, presentations contain explanations of the underlying reasons behind a technique. Importantly, this is one of the first statistics texts in the social sciences using R as the principal statistical package. Key features include the following. Conceptual Focus – The focus throughout is more on conceptual understanding and attainment of statistical literacy and thinking than on learning a set of tools and procedures. Problems and Cases – Chapters and sections open with examples of situations related to the forthcoming issues, and major sections ends with a case study. For example, after the section on describing relationships between variables, there is a worked case that demonstrates the analyses, presents computer output, and leads the student through an interpretation of that output. Continuity of Examples – A master data set containing nearly all of the data used in the book’s examples is introduced at the beginning of the text. This ensures continuity in the examples used across the text. Companion Website – A companion website contains instructions on how to use R, SAS, and SPSS to solve the end-of-chapter exercises and offers additional exercises. Field Tested – The manuscript has been field tested for three years at two leading institutions.

Introductory Statistics for Engineering Experimentation

Author : Peter R. Nelson
File Size : 36.8 MB
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A concise treatment for undergraduate and graduate students who need a guide to statistics that focuses specifically on engineering.

R and Statdisk

Author : Siamack Bondari
File Size : 73.2 MB
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Delve into the fascinating field of Statistics. Explore introductory level Statistics topics through the lens of how those calculations and decisions can be impacted by values, people, beliefs, and information in the world around us. Learn to calculate the mathematically correct solution and then explore whether the "correct" solutions is the "right" decision. Expand your analysis using publisher resources such as Statdisk, or free analysis tools such as programming with R.

Statistics

Author : Michael J. Crawley
File Size : 42.70 MB
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"...I know of no better book of its kind..." (Journal ofthe Royal Statistical Society, Vol 169 (1), January2006) A revised and updated edition of this bestselling introductorytextbook to statistical analysis using the leading free softwarepackage R This new edition of a bestselling title offers a conciseintroduction to a broad array of statistical methods, at a levelthat is elementary enough to appeal to a wide range ofdisciplines. Step-by-step instructions help thenon-statistician to fully understand the methodology. Thebook covers the full range of statistical techniques likely to beneeded to analyse the data from research projects, includingelementary material like t--tests and chi--squared tests,intermediate methods like regression and analysis of variance, andmore advanced techniques like generalized linear modelling. Includes numerous worked examples and exercises within eachchapter.

Introductory Statistics a Decision Map

Author : Thad R. Harshbarger
File Size : 74.47 MB
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Arithmetic and algebra; Flow charts and decision maps; Graphing; Characteristics of a distribution; Transformations of scale; Theoretical distributions; Probability; Hypothesis testing; Comparing proportions or entire distributions; Comparing variances; Comparing means: one or two samples; Comparing means: three or more samples; Hypothesis tests with ordinal scales; Prediction; Correlation: pearson and related formulas; Other two-variable correlation indices; Correlating three or more variables.

Introductory Statistics

Author : Lloyd R. Jaisingh
File Size : 50.47 MB
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Statistics with R

Author : Robert Stinerock
File Size : 41.20 MB
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The dynamic, student focused textbook provides step-by-step instruction in the use of R and of statistical language as a general research tool. It is ideal for anyone hoping to: Complete an introductory course in statistics Prepare for more advanced statistical courses Gain the transferable analytical skills needed to interpret research from across the social sciences Learn the technical skills needed to present data visually Acquire a basic competence in the use of R. The book provides readers with the conceptual foundation to use applied statistical methods in everyday research. Each statistical method is developed within the context of practical, real-world examples and is supported by carefully developed pedagogy and jargon-free definitions. Theory is introduced as an accessible and adaptable tool and is always contextualized within the pragmatic context of real research projects and definable research questions. Author Robert Stinerock has also created a wide range of online resources, including: R scripts, complete solutions for all exercises, data files for each chapter, video and screen casts, and interactive multiple-choice quizzes.

Introduction to Statistics Through Resampling Methods and R

Author : Phillip I. Good
File Size : 24.40 MB
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A highly accessible alternative approach to basic statistics Praise for the First Edition: "Certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see . . . it would make a good nightstand book for every statistician."—Technometrics Written in a highly accessible style, Introduction to Statistics through Resampling Methods and R, Second Edition guides students in the understanding of descriptive statistics, estimation, hypothesis testing, and model building. The book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote. The Second Edition utilizes the R programming language to simplify tedious computations, illustrate new concepts, and assist readers in completing exercises. The text facilitates quick learning through the use of: More than 250 exercises—with selected "hints"—scattered throughout to stimulate readers' thinking and to actively engage them in applying their newfound skills An increased focus on why a method is introduced Multiple explanations of basic concepts Real-life applications in a variety of disciplines Dozens of thought-provoking, problem-solving questions in the final chapter to assist readers in applying statistics to real-life applications Introduction to Statistics through Resampling Methods and R, Second Edition is an excellent resource for students and practitioners in the fields of agriculture, astrophysics, bacteriology, biology, botany, business, climatology, clinical trials, economics, education, epidemiology, genetics, geology, growth processes, hospital administration, law, manufacturing, marketing, medicine, mycology, physics, political science, psychology, social welfare, sports, and toxicology who want to master and learn to apply statistical methods.

Mathematics for Introductory Statistics

Author : Andrew R. Baggaley
File Size : 54.79 MB
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Algebra; Plotting points and linear equations on graphs; Extraction of the square root; Use of the simmulation operator and summation laws.

Introductory Statistics for the Behavioral Sciences

Author : Joan Welkowitz
File Size : 26.28 MB
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A comprehensive and user-friendly introduction to statistics forbehavioral science students—revised and updated Refined over seven editions by master teachers, this book givesinstructors and students alike clear examples and carefully craftedexercises to support the teaching and learning of statistics forboth manipulating and consuming data. One of the most popular and respected statistics texts in thebehavioral sciences, the Seventh Edition of Introductory Statisticsfor the Behavioral Sciences has been fully revised. The new editionpresents all the topics students in the behavioral sciences need ina uniquely accessible and easy-to-understand format, aiding in thecomprehension and implementation of the statistical analyses mostcommonly used in the behavioral sciences. The Seventh Edition features: A continuous narrative that clearly explains statistics whiletracking a common data set throughout, making the conceptsunintimidating and memorable, and providing a framework thatconnects all of the topics and allows for easy comparison ofdifferent statistical analyses Coverage of important aspects of research design throughout thetext, such as the "correlation is not causality" principle Updated and annotated SPSS output at the end of each chapterwith step-by-step instructions Updated examples and exercises An expanded website, at www.wiley.com/go/welkowitz, with testbank, chapter quizzes, and PowerPoint slides for instructors, aswell as a second website for students with additional basic mathcoverage, math review exercises, a study guide, a set of additionalSPSS exercises, and more downloadable data sets

Analyzing Linguistic Data

Author : R. H. Baayen
File Size : 68.97 MB
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Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.

Understanding Statistics Using R

Author : Randall Schumacker
File Size : 54.13 MB
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This book was written to provide resource materials for teachers to use in their introductory or intermediate statistics class. The chapter content is ordered along the lines of many popular statistics books so it should be easy to supplement the content and exercises with class lecture materials. The book contains R script programs to demonstrate important topics and concepts covered in a statistics course, including probability, random sampling, population distribution types, role of the Central Limit Theorem, creation of sampling distributions for statistics, and more. The chapters contain T/F quizzes to test basic knowledge of the topics covered. In addition, the book chapters contain numerous exercises with answers or solutions to the exercises provided. The chapter exercises reinforce an understanding of the statistical concepts presented in the chapters. An instructor can select any of the supplemental materials to enhance lectures and/or provide additional coverage of concepts and topics in their statistics book.

Introductory R A Beginner s Guide to Data Visualisation Statistical Analysis and Programming in R

Author : Robert Knell
File Size : 45.67 MB
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R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a little about statistics but who hasn't yet got to grips with the ways of R. This new edition is completely revised and greatly expanded with new chapters on the basics of descriptive statistics and statistical testing, considerably more information on statistics and six new chapters on programming in R. Topics covered include: A walkthrough of the basics of R's command line interface Data structures including vectors, matrices and data frames R functions and how to use them Expanding your analysis and plotting capacities with add-in R packages A set of simple rules to follow to make sure you import your data properly An introduction to the script editor and advice on workflow A detailed introduction to drawing publication-standard graphs in R How to understand the help files and how to deal with some of the most common errors that you might encounter. Basic descriptive statistics The theory behind statistical testing and how to interpret the output of statistical tests Thorough coverage of the basics of data analysis in R with chapters on using chi-squared tests, t-tests, correlation analysis, regression, ANOVA and general linear models What the assumptions behind the analyses mean and how to test them using diagnostic plots Explanations of the summary tables produced for statistical analyses such as regression and ANOVA Writing your own functions in R Using table operations to manipulate matrices and data frames Using conditional statements and loops in R programmes. Writing longer R programmes. The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.

Formula table Card for Weiss Hassett s Introductory Statistics 3 E

Author : Larry R. Griffey
File Size : 71.4 MB
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An Introduction to Basic Statistics for Biologists Using R

Author : Colin D. Macleod
File Size : 20.89 MB
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This wrokbook provides biologists with an easy-to-follow introduction to conducting statistical analysis in R. It does this through a series of practical exercises based on easy-to-follow flow diagrams that show biologists exactly how to do a variety of key tasks.

An Introduction to Statistical Methods and Data Analysis

Author : R. Lyman Ott
File Size : 26.59 MB
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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.