Statistics for Bioengineering Sciences: With MATLAB and WinBUGS Support

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Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing engineering fields, bioengineering and biomedical engineering, while implementing software that engineers are familiar with.

The author integrates introductory statistics for engineers and introductory biostatistics as a single textbook heavily oriented to computation and hands on approaches. For example, topics ranging from the aspects of disease and device testing, Sensitivity, Specificity and ROC curves, Epidemiological Risk Theory, Survival Analysis, or Logistic and Poisson Regressions are covered.

In addition to the synergy of engineering and biostatistical approaches, the novelty of this book is in the substantial coverage of Bayesian approaches to statistical inference. Many examples in this text are solved using both the traditional and Bayesian methods, and the results are compared and commented.

Author(s): Brani Vidakovic (auth.)
Series: Springer Texts in Statistics
Edition: 1
Publisher: Springer-Verlag New York
Year: 2011

Language: English
Pages: 753
Tags: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences

Front Matter....Pages i-xvi
Introduction....Pages 1-7
The Sample and Its Properties....Pages 9-58
Probability, Conditional Probability, and Bayes’ Rule....Pages 59-107
Sensitivity, Specificity, and Relatives....Pages 109-130
Random Variables....Pages 131-189
Normal Distribution....Pages 191-228
Point and Interval Estimators....Pages 229-277
Bayesian Approach to Inference....Pages 279-315
Testing Statistical Hypotheses....Pages 317-354
Two Samples....Pages 355-408
ANOVA and Elements of Experimental Design....Pages 409-476
Distribution-Free Tests....Pages 477-501
Goodness-of-Fit Tests....Pages 503-530
Models for Tables....Pages 531-570
Correlation....Pages 571-597
Regression....Pages 599-656
Regression for Binary and Count Data....Pages 657-700
Inference for Censored Data and Survival Analysis....Pages 701-731
Bayesian Inference Using Gibbs Sampling – BUGS Project....Pages 733-745
Back Matter....Pages 747-753