This guide to biostatistics takes an entertaining approach. Students should benefit not only from the humorous aspects, but also the in-depth discussions. Topics include: data basics - normal distribution, probability, statistical inference; analysis of variance - t-test, paired t-test, one-way, factorial, and repeated-measures ANOVA; simple and multiple regression, regression and ANOVA, correlation, and factor analysis; and nonparametric statistics - tests of significance for categorical frequency data and ranked data, measures of association for categorical data and ranked data. Each section has a "Circular Reasoning and Anti-intellectual Pomposity" (C.R.A.P.) detector, and "Computer Notes" provide specific commands and general advice for SPSS, BMDP and Minitab.

Product Description: Graphs, networks and agent-based modeling are the most thriving and attracting sciences used in ecology and environmental sciences. As such, this book is the first comprehensive treatment of the subject in the areas of ecology and environmental sciences...read more

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests(TM), neural nets, support vector machines, nearest neighbors and boosting.