MSMS 5010, BCOR 5570 Biostatistics
This guide covers material for Biostatistics I (MSMS 5010 and BCOR 5570).
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Biostatistics
Biostatistics
MSMS 5010, BCOR 5570
Fall 2024
This graduate course will introduce the most commonly used statistical tests and procedures to analyze biological and ecological data. The main objective is to prepare the students to identify the most correct statistics to analyze biological data, perform the statistical analysis in R and correctly interpret the results. Lectures will consist of short theoretical presentations followed by a lab where students will do guided exercises in R. Students will be required to do readings prior to the class on the theoretical basis of the theme of the week, and perform unguided exercises (homework) to cement knowledge.
Required Textbooks:
Experimental Design and Data Analysis for Biologists by
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
Call Number: QH323.5 .Q85 2002ISBN: 0521811287Publication Date: 2002-03-21- Experimental Design and Data Analysis for Biologists by
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
Call Number: E-BOOKISBN: 0521811287Publication Date: 2002-03-21
Recommended Textbooks:
Introductory Statistics with R by
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.
Call Number: QA276.45.R3 D33 2008ISBN: 0387790535Publication Date: 2008-08-15
The R Book by
Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: '...if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.' (The American Statistician, August 2008) 'The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book...' (Professional Pensions, July 2007)
Call Number: E-BOOKISBN: 9781118448960Publication Date: 2012-11-07
Biostatistical Design and Analysis Using R by
R -- the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research. Topics covered include: simple hypothesis testing, graphing exploratory data analysis and graphical summaries regression (linear, multi and non-linear) simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures) frequency analysis and generalized linear models. Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques. The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.
Call Number: E-BOOKISBN: 9781405190084Publication Date: 2010-05-10
At the conclusion of this course, the student will be able to:
- Describe critical aspects of data such as scales of measurement.
- Identify if the variables are discrete or continuous.
- Calculate summary statistics.
- Create appropriate graphs, charts, maps, and tables to represent data.
- Determine the appropriateness of various statistical tests and procedures for analyzing different types of biological data.
- Analyze biological data using the appropriate statistical test(s) in software R.
- Correctly interpret results of statistical analysis.
- Write a grammatically perfect, technically informed, well-structured document with regards to data analysis.
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