Easy Way To Learn Biostatistics
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Biostatistics For Dummies
Break down biostatistics, make sense of complex concepts, and pass your class If you're taking biostatistics, you may need or want a little extra assistance as you make your way through. Biostatistics For Dummies follows a typical biostatistics course at the college level, helping you understand even the most difficult concepts, so you can get the grade you need. Start at the beginning by learning how to read and understand mathematical equations and conduct clinical research. Then, use your knowledge to analyze and graph your data. This new edition includes more example problems with step-by-step walkthroughs on how to use statistical software to analyze large datasets. Biostatistics For Dummies is your go-to guide for making sense of it all. Review basic statistics and decode mathematical equations Learn how to analyze and graph data from clinical research studies Look for relationships with correlation and regression Use software to properly analyze large datasets Anyone studying in clinical science, public health, pharmaceutical sciences, chemistry, and epidemiology-related fields will want this book to get through that biostatistics course.
Easy Interpretation of Biostatistics E-Book
Author: Gail F. Dawson
language: en
Publisher: Elsevier Health Sciences
Release Date: 2012-01-02
Learn biostatistics the easy way. This outstanding resource presents the key concepts you need to understand biostatistics and how to apply them in clinical medicine. Easy-to-understand examples and analogies explain complex concepts, and practical applications provide you with real tools for use in daily practice. The book's organization is intuitive, so that concepts build upon one another, maximizing understanding. This book will give you the confidence to appraise the existing literature - and the vocabulary you need to discuss it. - Uses an easy-to-understand presentation and writing style to make the material easily accessible. - Places its emphasis on concepts, not formulas, for more clinical-based guidance. - Focuses on practical applications of biostatistics to medical practice to give you a better understanding of how and why research is conducted. - Presents concise but comprehensive coverage to create easily accessible yet complete information. - Provides examples, analogies, and memorization tips to make the material easier to absorb.
Using R for Biostatistics
Author: Thomas W. MacFarland
language: en
Publisher: Springer Nature
Release Date: 2021-03-02
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.