This book covers some introductory steps in biostatistics using R programming language. Biostatistics is the branch of statistics that applies statistical methods to medical and biological problems. Biostatistics has become more important recently for studying the great amount of data that is produced from census data, genome sequencing, gene expression data, medical bioinformatics, and medical imaging data. With the help of R programming, statistical analysis, data cleaning, data visualization, and machine learning has ...
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This book covers some introductory steps in biostatistics using R programming language. Biostatistics is the branch of statistics that applies statistical methods to medical and biological problems. Biostatistics has become more important recently for studying the great amount of data that is produced from census data, genome sequencing, gene expression data, medical bioinformatics, and medical imaging data. With the help of R programming, statistical analysis, data cleaning, data visualization, and machine learning has become a relatively easy tasks for these huge datasets. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all these tasks, but also, has additional packages that were specifically designed for several statistical tasks related to biology and medical data. In addition, many scientific journals require the data analysis R scripts to ensure reproducibility of the submitted results. The first chapter of this book introduces many statistical concepts used in scientific research like study designs, sample, and population, and data types. Chapters 2, 4, and 5 cover the three main data types which are continuous data, categorical data, and time to event data. Chapter 3 discusses the popular continuous distribution that is the normal distribution along with its application to sample data. Chapter 6 is about the sampling distribution of different sample estimates along with a discussion of the famous central limit theorem (CLT). Chapters 7 and 8 are involved in confidence interval (CI) calculations, and Chapters 9-11 discuss several types of statistical tests like t-test, ANOVA, Chi-square, log-rank, etc. Finally, Chapters 12-14 deal with different regression types; linear regression for continuous outcomes, logistic regression for binary outcomes, and Cox regression for time to event outcomes. In all these chapters, many examples from many scientific journal articles or built in data sets along with different codes and outputs are given to help your understanding of these numerous statistical concepts. I hope this book will be a great addition to your future biostatistical projects.
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Add this copy of Introduction to Biostatistics Using R to cart. $106.85, new condition, Sold by discount_scientific_books rated 5.0 out of 5 stars, ships from Sterling Heights, MI, UNITED STATES, published 2021 by Arcler Press.
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Fine. Sewn binding. Cloth over boards. 518 p. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Add this copy of Introduction to Biostatistics Using R to cart. $186.82, new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2021 by Arcler Press.
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New. Sewn binding. Cloth over boards. 518 p. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
