A Second Course in Statistics. Regression Analysis (Fifth Edition), William Mendenhall

Expanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and computing to teach introductory statistics to students in the life sciences. This popular textbook covers the mathematics underlying classical statistical analysis, the modeling aspects of statistical analysis and the biological interpretation of results, and the application of statistical software in analyzing real-world problems and datasets.

New to the Second Edition

• A new chapter on non-linear regression models
• A new chapter that contains examples of complete data analyses, illustrating how a full-fledged statistical analysis is undertaken
• Additional exercises in most chapters
• A summary of statistical formulas related to the specific designs used to teach the statistical concepts

This text provides a computational toolbox that enables students to analyze real datasets and gain the confidence and skills to undertake more sophisticated analyses. Although accessible with any statistical software, the text encourages a reliance on R. For those new to R, an introduction to the software is available in an appendix. The book also includes end-of-chapter exercises as well as an entire chapter of case exercises that help students apply their knowledge to larger datasets and learn more about approaches specific to the life sciences.

Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.

New to the Second Edition

• The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
• New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
• New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
• New chapter on simulation that includes examples of data generated from complex models and distributions
• A detailed discussion of the philosophy and use of the knitr and markdown packages for R
• New packages that extend the functionality of R and facilitate sophisticated analyses
• Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots

Easily Find Your Desired Task

Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.

Peter Goos, University of Leuven and University of Antwerp, Belgium

David Meintrup, University of Applied Sciences Ingolstadt, Germany

A first course on basic statistical methodology using JMP

This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software.

Key features:

• Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested.
• Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values).
• Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic.
• Promotes the use of graphs and confidence intervals in addition to p-values.
• Course materials and tutorials for teaching are available on the book's companion website.

Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.