
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Provides a one-stop resource for engineers learning biostatistics using MATLABĀ® and WinBUGS
Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing bio-oriented engineering fields while implementing software packages that are familiar to engineers. The book is heavily oriented to computation and hands-on approaches so readers understand each step of the programming. Another dimension of this book is in parallel coverage of both Bayesian and frequentist approaches to statistical inference. It avoids taking sides on the classical vs. Bayesian paradigms, and many examples in this book are solved using both methods. The results are then compared and commented upon. Readers have the choice of MATLABĀ® for classical data analysis and WinBUGS/OpenBUGS for Bayesian data analysis. Every chapter starts with a box highlighting what is covered in that chapter and ends with exercises, a list of software scripts, datasets, and references.
Engineering Biostatistics: An Introduction using MATLABĀ® and WinBUGS also includes:
- parallel coverage of classical and Bayesian approaches, where appropriate
- substantial coverage of Bayesian approaches to statistical inference
- material that has been classroom-tested in an introductory statistics course in bioengineering over several years
- exercises at the end of each chapter and an accompanying website with full solutions and hints to some exercises, as well as additional materials and examples
Engineering Biostatistics: An Introduction using MATLABĀ® and WinBUGSĀ can serve as a textbook for introductory-to-intermediate applied statistics courses, as well as a useful reference for engineers interested in biostatistical approaches.
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Information
Table of contents
- Cover
- Wiley Series
- Title Page
- Copyright
- Preface
- Chapter 1: Introduction
- Chapter 2: The Sample and Its Properties
- Chapter 3: Probability, Conditional Probability, and Bayesā Rule
- Chapter 4: Sensitivity, Specificity, and Relatives
- Chapter 5: Random Variables
- Chapter 6: Normal Distribution
- Chapter 7: Point and Interval Estimators
- Chapter 8: Bayesian Approach to Inference
- Chapter 9: Testing Statistical Hypotheses
- Chapter 10: Two Samples
- Chapter 11: ANOVA and Elements of Experimental Design
- Chapter 12: Models for Tables
- Chapter 13: Correlation
- Chapter 14: Regression
- Chapter 15: Regression for Binary and Count Data
- Chapter 16: Inference for Censored Data and Survival Analysis
- Chapter 17: Goodness-of-Fit Tests
- Chapter 18: Distribution-Free Methods
- Chapter 19: Bayesian Inference Using Gibbs Sampling - BUGS Project
- Index
- EULA