Biostatistics explored through R software: An overview PDF Download

Author: Vinaitheerthan Renganathan
Publisher: Vinaitheerthan Renganathan
ISBN: 935493658X
Category : Medical
Languages : en
Pages : 107

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Book Description
The term Statistics and Data is now familiar to most of us even though we are related to different fields such as Clinical, Biology, Business & Management, Engineering and Physical Sciences. Statistical methods are being applied in different fields including the emerging fields such as Machine Learning, Natural Language Processing and Artificial Intelligence because of the fact that each field deals with data in one form or the other. Statistical methods help decision makers to arrive at evidence based decisions by collecting, analyzing and interpreting the data. Biostatistics is a branch of statistics which is applied in the biomedical domain and it includes clinical and biological fields. Biostatistical methods include descriptive, inferential and Statistical modeling tools. Descriptive statistical tools help us to draw information and summary about the data in terms of measures of central tendency, measures of dispersion, measure of skewness and underlying probability distributions. Inferential Statistical tools help us to estimate, test the hypothesis and draw inferences about the parameters of the data. Statistical modeling tools help us to study and estimate the relationship between the variables. The book starts with the introduction of concepts in biostatistics and moves on to explore descriptive and inferential statistical tools using R Open Source Software. It then explores statistical modeling tools such as regression analysis and survival analysis. The book also covers the role of Biostatistics in the clinical trials process. Clinical trials are scientific experiments which are conducted in different phases. It involves testing of drugs and clinical procedures for treating different diseases and conditions among a sample of participants before rolling out in the market. Biostatistics tools help clinical trial researchers in deciding about the study design, sample size and analytical methods. The data set used in the book can be downloaded from Author’s Website.

Author: Babak Shahbaba
Publisher: Springer Science & Business Media
ISBN: 1461413028
Category : Medical
Languages : en
Pages : 355

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Book Description
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.

Author: Thomas W. MacFarland
Publisher: Springer Nature
ISBN: 3030624048
Category : Medical
Languages : en
Pages : 929

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Book Description
This book introduces the open source R software language that can be implemented in biostatistics for data organization, statistical analysis, and graphical presentation. In the years since the authors’ 2014 work Introduction to Data Analysis and Graphical Presentation in Biostatistics with R, the R user community has grown exponentially and the R language has increased in maturity and functionality. This updated volume expands upon skill-sets useful for students and practitioners in the biological sciences by describing how to work with data in an efficient manner, how to engage in meaningful statistical analyses from multiple perspectives, and how to generate high-quality graphics for professional publication of their research. A common theme for research in the diverse biological sciences is that decision-making depends on the empirical use of data. Beginning with a focus on data from a parametric perspective, the authors address topics such as Student t-Tests for independent samples and matched pairs; oneway and twoway analyses of variance; and correlation and linear regression. The authors also demonstrate the importance of a nonparametric perspective for quality assurance through chapters on the Mann-Whitney U Test, Wilcoxon Matched-Pairs Signed-Ranks test, Kruskal-Wallis H-Test for Oneway Analysis of Variance, and the Friedman Twoway Analysis of Variance. To address the element of data presentation, the book also provides an extensive review of the many graphical functions available with R. There are now perhaps more than 15,000 external packages available to the R community. The authors place special emphasis on graphics using the lattice package and the ggplot2 package, as well as less common, but equally useful, figures such as bean plots, strip charts, and violin plots. A robust package of supplementary material, as well as an introduction of the development of both R and the discipline of biostatistics, makes this ideal for novice learners as well as more experienced practitioners.

Author: Thomas Glover
Publisher: Waveland Press
ISBN: 1478631112
Category : Mathematics
Languages : en
Pages : 553

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Book Description
For over a decade, Glover and Mitchell have provided life-sciences students with an accessible, complete introduction to the use of statistics in their disciplines. The authors emphasize the relationships between probability, probability distributions, and hypothesis testing using both parametric and nonparametric analyses. Copious examples throughout the text apply concepts and theories to real questions faced by researchers in biology, environmental science, biochemistry, and health sciences. Dozens of examples and problems are new to the Third Edition, as are “Concept Checks”—short questions that allow readers to immediately gauge their mastery of the topics presented. Regardless of mathematical background, all readers will appreciate the value of statistics as a fundamental quantitative skill for the life sciences.

Author: Måns Thulin
Publisher: BoD - Books on Demand
ISBN: 9152701514
Category : Mathematics
Languages : en
Pages : 598

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Book Description
The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you: - Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. - Exploratory data analysis - using visualisation and multivariate techniques to explore datasets. - Statistical inference - modern methods for testing hypotheses and computing confidence intervals. - Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. - Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. - Ethics in statistics - ethical issues and good statistical practice. - R programming - writing code that is fast, readable, and free from bugs. Starting from the very basics, Modern Statistics with R helps you learn R by working with R. Topics covered range from plotting data and writing simple R code to using cross-validation for evaluating complex predictive models and using simulation for sample size determination. The book includes more than 200 exercises with fully worked solutions. Some familiarity with basic statistical concepts, such as linear regression, is assumed. No previous programming experience is needed.

Author: Thomas W. MacFarland
Publisher: Springer Science & Business Media
ISBN: 3319025325
Category : Medical
Languages : en
Pages : 167

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Book Description
Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressing scientific issue for those engaged in biostatistics. Readers will be equipped to run analyses and make graphical presentations based on the sample dataset and their own data. The hands-on approach will benefit students and ensure the accessibility of this book for readers with a basic understanding of R. Topics include: an introduction to Biostatistics and R, data exploration, descriptive statistics and measures of central tendency, t-Test for independent samples, t-Test for matched pairs, ANOVA, correlation and linear regression, and advice for future work.

Author: Vinaitheerthan Renganathan
Publisher: Vinaitheerthan Renganathan
ISBN: 9356201188
Category : Social Science
Languages : en
Pages : 100

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Book Description
Statistical methods are being used in different fields such as Business & Economics, Engineering, Clinical & Pharmaceutical research including the emerging fields such as Machine Learning and Artificial Intelligence. Statistical methods based on the traditional frequentist approach are currently being use in these fields. With the emergence of high end computing nowadays Bayesian approach to Statistical Methods also being used in different fields. Bayesian approach involves prior, likelihood and posterior concepts in carrying out the statistical analysis. Bayesian methods assume model parameters as random as opposed to fixed in frequentist approach. It is useful even when the sample size is small. One of the drawbacks of Bayesian method is it involves subjectivity in carrying out the analysis. With the availability of advanced computing technologies, implementation of Bayesian methods is possible using Markov Chain Monte Carlo (MCMC) methods. This book provides an overview of Bayesian approaches to statistical methods and uses open source software R for carrying out analysis using sample data sets which can be downloaded from author’s website.

Author: EDITOR IJSMI
Publisher: createspace
ISBN: 1725921227
Category : Medical
Languages : en
Pages : 67

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Book Description
This book intends to provide an overview of biostatistics concepts and methodology through the use of statistical software. It helps clinicians, health care and biomedical professionals who need to have basic knowledge of biostatistics as they come across clinical data related to patient, drug and dosage requirement, treatment modalities in day to day life and they are required to take clinical and health care decisions based on the data. This book covers basic concepts involved in the field of Biostatistics such as descriptive statistics, inferential statistics, correlation and regression along with the advanced concepts such as factor analysis, cluster analysis, discriminant analysis and survival analysis. Each topic is explained with the help of R statistical package (open source package). One important note that the book will not discuss about the formulas and equations involved in the statistical concepts and the author assumes that the readers have basic understanding of excel as the sample dataset is used in the book are mostly excel based datasets and also have some clinical background.

Author: John Verzani
Publisher: CRC Press
ISBN: 1315360306
Category : Computers
Languages : en
Pages : 522

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Book Description
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.

Author: Peter Dalgaard
Publisher: Springer Science & Business Media
ISBN: 0387790543
Category : Mathematics
Languages : en
Pages : 364

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Book Description
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.