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Title Using R for Introductory Statistics
Author John Verzani
Publisher CRC Press
Release 2018-10-03
Category Mathematics
Total Pages 518
ISBN 1315360306
Language English, Spanish, and French
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Book Summary:

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.

Introductory Statistics with R by Peter Dalgaard

Title Introductory Statistics with R
Author Peter Dalgaard
Publisher
Release 2008
Category Bioinformatics
Total Pages
ISBN 9780387570655
Language English, Spanish, and French
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Book Summary:

Introductory Statistics by William B. Ware

Title Introductory Statistics
Author William B. Ware
Publisher Routledge
Release 2013-02-15
Category Education
Total Pages 520
ISBN 1136870105
Language English, Spanish, and French
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Book Summary:

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.

Title Beginne R Introductory Statistics Using R
Author Darrin Thomas
Publisher SuJinSoLa
Release
Category Education
Total Pages
ISBN
Language English, Spanish, and French
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Book Summary:

Statistics is a challenging subject. Add to this the challenge of computer coding and many would be ready to give up. In this text, Darrin Thomas explains basic concepts of statistics within the framework of using R. The blending of statistics and computer coding has quickly become a standard in research to in both academia and industry. As such, the concepts in this text are pertinent for the 21 st century.

Introductory Statistics with R by Peter Dalgaard

Title Introductory Statistics with R
Author Peter Dalgaard
Publisher Springer Science & Business Media
Release 2008-08-15
Category Mathematics
Total Pages 364
ISBN 0387790535
Language English, Spanish, and French
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Book Summary:

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.

Title Using R for Data Analysis in Social Sciences
Author Quan Li
Publisher Oxford University Press
Release 2018
Category Business & Economics
Total Pages 366
ISBN 0190656212
Language English, Spanish, and French
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Book Summary:

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.

Statistics in Toxicology Using R by Ludwig A. Hothorn

Title Statistics in Toxicology Using R
Author Ludwig A. Hothorn
Publisher CRC Press
Release 2016-01-13
Category Mathematics
Total Pages 234
ISBN 1498701280
Language English, Spanish, and French
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Book Summary:

The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover, recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas, Statistics in Toxicology Using R explains the statistical analysis of selected experimental data in toxicology and presents assay-specific suggestions, such as for the in vitro micronucleus assay. Mostly focusing on hypothesis testing, the book covers standardized bioassays for chemicals, drugs, and environmental pollutants. It is organized according to selected toxicological assays, including: Short-term repeated toxicity studies Long-term carcinogenicity assays Studies on reproductive toxicity Mutagenicity assays Toxicokinetic studies The book also discusses proof of safety (particularly in ecotoxicological assays), toxicogenomics, the analysis of interlaboratory studies and the modeling of dose-response relationships for risk assessment. For each toxicological problem, the author describes the statistics involved, matching data example, R code, and outcomes and their interpretation. This approach allows you to select a certain bioassay, identify the specific data structure, run the R code with the data example, understand the test outcome and interpretation, and replace the data set with your own data and run again.

Title Biostatistics for Epidemiology and Public Health Using R
Author Bertram K.C. Chan, PhD
Publisher Springer Publishing Company
Release 2015-11-05
Category Medical
Total Pages 500
ISBN 0826110266
Language English, Spanish, and French
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Book Summary:

Since it first appeared in 1996, the open-source programming language R has become increasingly popular as an environment for statistical analysis and graphical output. This is the first textbook to present classical biostatistical analysis for epidemiology and related public health sciences to students using the R language. Based on the assumption that readers have minimal familiarity with statistical concepts, the author uses a step-by-step approach to building skills. The text encompasses biostatistics from basic descriptive and quantitative statistics to survival analysis and missing data analysis in epidemiology. Illustrative examples, including real-life research problems drawn from such areas as nutrition, environmental health, and behavioral health, engage students and reinforce the understanding of R. These examples illustrate the replication of R for biostatistical calculations and graphical display of results. The text covers both essential and advanced techniques and applications in biostatistics that are relevant to epidemiology. Also included are an instructor's guide, student solutions manual, and downloadable data sets. Key Features: First overview biostatistics textbook for epidemiology and public health that uses the open-source R program Covers essential and advanced techniques and applications in biostatistics as relevant to epidemiology Features abundant examples to illustrate the application of R language for biostatistical calculations and graphical displays of results Includes instructor's guide, student solutions manual, and downloadable data sets.

Title Statistical Methods for Hospital Monitoring with R
Author Anthony Morton
Publisher John Wiley & Sons
Release 2013-06-27
Category Medical
Total Pages 432
ISBN 1118639170
Language English, Spanish, and French
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Book Summary:

Hospitals monitoring is becoming more complex and is increasingboth because staff want their data analysed and because ofincreasing mandated surveillance. This book provides a suiteof functions in R, enabling scientists and data analysts working ininfection management and quality improvement departments inhospitals, to analyse their often non-independent data which isfrequently in the form of trended, over-dispersed and sometimesauto-correlated time series; this is often difficult to analyseusing standard office software. This book provides much-needed guidance on data analysis using Rfor the growing number of scientists in hospital departments whoare responsible for producing reports, and who may have limitedstatistical expertise. This book explores data analysis using R and is aimed atscientists in hospital departments who are responsible forproducing reports, and who are involved in improving safety.Professionals working in the healthcare quality and safetycommunity will also find this book of interest Statistical Methods for Hospital Monitoring with R: Provides functions to perform quality improvement and infectionmanagement data analysis. Explores the characteristics of complex systems, such asself-organisation and emergent behaviour, along with theirimplications for such activities as root-cause analysis and thePareto principle that seek few key causes of adverse events. Provides a summary of key non-statistical aspects of hospitalsafety and easy to use functions. Provides R scripts in an accompanying web site enablinganalyses to be performed by the reader ahref="http://www.wiley.com/go/hospital_monitoring"http://www.wiley.com/go/hospital_monitoring/a Covers issues that will be of increasing importance in thefuture, such as, generalised additive models, and complex systems,networks and power laws.

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