Introduction to the Practice of Statistics (IPS) shows students how to produce and interpret data from real-world contexts doing the same type of data gathering and analysis that working statisticians in all kinds of businesses and institutions do every day. With this phenomenally successful approach originally developed by David Moore and George McCabe, statistics is more than just a collection of techniques and formulas. Instead, students develop a systematic way of thinking about data, with a focus on problem-solving that helps them understand statistical concepts and master statistical reasoning. Maximize Teaching and Learning with WebAssign Premium Macmillan Learning and WebAssign have partnered to deliver WebAssign Premium a comprehensive and flexible suite of resources for your introductory statistics course. Combining the most widely used online homework platform with authoritative textbook content and Macmillan s esteemed Stattools, WebAssign Premium extends and enhances the classroom experience for instructors and students. Preview course content and sample assignments at www.webassign.net/whfreeman. "
The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover, recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas, Statistics in Toxicology Using R explains the statistical analysis of selected experimental data in toxicology and presents assay-specific suggestions, such as for the in vitro micronucleus assay. Mostly focusing on hypothesis testing, the book covers standardized bioassays for chemicals, drugs, and environmental pollutants. It is organized according to selected toxicological assays, including: * Short-term repeated toxicity studies * Long-term carcinogenicity assays * Studies on reproductive toxicity * Mutagenicity assays * Toxicokinetic studies The book also discusses proof of safety (particularly in ecotoxicological assays), toxicogenomics, the analysis of interlaboratory studies and the modeling of dose-response relationships for risk assessment. For each toxicological problem, the author describes the statistics involved, matching data example, R code, and outcomes and their interpretation. This approach allows you to select a certain bioassay, identify the specific data structure, run the R code with the data example, understand the test outcome and interpretation, and replace the data set with your own data and run again.
Secure Wrap Articles
Secure Wrap Books