Buch, Englisch, 520 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 1120 g
Applications for the Clinical and Pharmaceutical Industries
Buch, Englisch, 520 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 1120 g
Reihe: Chapman & Hall/CRC Biostatistics Series
ISBN: 978-1-57444-610-4
Verlag: Taylor & Francis Ltd
Carefully designed for use by clinical and pharmaceutical researchers and scientists, Handbook of Regression Analysis and Modeling explores statistical methods that have been adapted into biological applications for the quickly evolving field of biostatistics. The author clearly delineates a six-step method for hypothesis testing using data that mimics real life. Relying heavily on computer software, he includes exploratory data analysis to evaluate the fit of the model to the actual data.
The book presents a well-defined procedure for adding or subtracting independent variables to the model variable and covers how to apply statistical forecasting methods to the serially correlated data characteristically found in clinical and pharmaceutical settings. The stand alone chapters allow you to pick and choose which chapter to read first and home in on the information that fits your immediate needs. Each example is presented in computer software format. The author uses MINITAB in the book but supplies instructions for SAS and SPSSX, making the book easily adaptable to individual situations.
Although written with the assumption that the reader has knowledge of basic and matrix algebra, the book supplies a short course on matrix algebra in the appendix for those who need it. Covering more than just statistical theory, the book provides advanced methods that you can put to immediate use.
Zielgruppe
Professional
Autoren/Hrsg.
Weitere Infos & Material
Basic Statistical Concepts. Simple Linear Regression. Special Problems in Simple Linear Regression. Some Aspects and Examples in Constructing a Valid Simple Regression Study. Multiple Linear Regression. Correlation in Multiple Regression. Issues in Multiple Linear Regression. Polynomial Regression. Special Topics in Multiple Regression. Indicator (Dummy) Variable Regression. Model Building/Model Selection. Analysis of Covariance. Logistic Regression.
