Chapman and Hall/CRC
520 pages | 174 B/W Illus.
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.
"Unlike many other statistical texts, Paulson's book is easy to read and comprehend. It moves the reader along the statistics learning path with much less trauma. The author takes great care to explain the logic behind the statistical analysis process as well as disclose the benefits associated with the effort. Paulson's addition of carefully selected philosophic nuggets gives certain passages of the text a delightful 'Zen' quality that is very refreshing. I enjoyed the reading experience and will use the text in my own work."
- Lawton A. Seal, Healthpoint/DEB Pharmaceuticals
"The author has done a commendable job of combining theory and practice. The writing is clear and supplemented with numerous worked examples. Furthermore, the clear figures and tables make the text well suited for the classroom and as a reference text for the basic scientist whose research requires regression analysis . . . I would recommend this text as a primary regression text for the applied researcher."
– Gregory E. Gilbert, Medical University of South Carolina, in The American Statistician, February 2009, Vol. 63, No. 1
Basic Statistical Concepts
Simple Linear Regression
Special Problems in Simple Linear Regression: Determining ˆx from y, Serial Correlation, and Curve Fitting
Some Aspects and Examples in Constructing a Valid Simple Regression Study
Multiple Linear Regression
Correlation in Multiple Regression
Issues in Multiple Linear Regression
Special Topics in Multiple Regression
Indicator (Dummy) Variable Regression
Model Building/Model Selection
Analysis of Covariance