Statistics for Bioengineering Sciences

Brani Vidakovic is Fellow of American Statistical Association, Elected Member of International Statistical Institute, an Editor-in-Chief of Encyclopedia of Statistical Sciences, Second Edition, an Associate Editor of: Journal of American Statistical Association, Communications in Statistics, Annals of Institute of Statistical Mathematics, and Bayesian Statistics. He is Jointly Appointed Professor in School of Industrial and Systems Engineering - ISyE and Department of Biostatistics at Emory University and Adjunct Professor in Jiann-Ping Hsu College of Public Health, Georgia Southern University. Member of Integrative BioSystems Institute (IBSI), at Georgia Institute of Technology. Center for Bioinformatics and Computational Biology, at Biology Department, Georgia Institute of Technology.

From the book reviews: "This text has resulted from the author's teaching of introductory statistics to engineering students in the USA. Dealing both with the theoretical aspects of statistical methods and the need to implement software that engineers are familiar with, the book is a delight to read. ... I recommend the book to any one intending to use either MATLAB or/and WinBUGS for statistical modelling and analysis." (Carl M. O'Brien, International Statistical Review, Vol. 81 (3), 2014) "Although there are many engineering statistics books, this is the first one I have seen devoted to bioengineering. It is a very comprehensive book with many good features. ... I would say that Statistics for Bioengineering Sciences would make a wonderful text for a first course in statistics for biomedical engineering students and is a great reference for engineers and statisticians." (Michael R. Chernick, Technometrics, Vol. 55 (1), February, 2013)

• Introduction.- The Sample and Its Properties.- Probability, Conditional Probability, and Bayes' Rule.- Sensitivity, Specificity, and Relatives.- Random Variables.- Normal Distribution.- Point and Interval Estimators.- Bayesian Approach to Inference.- Testing Statistical Hypotheses.- Two Samples.- ANOVA and Elements of Experimental Design.- Distribution-Free Tests.- Goodness-of-Fit Tests.- Models for Tables.- Correlation.- Regression.- Regression for Binary and Count Data.- Inference for Censored Data and Survival Analysis.- Bayesian Inference Using Gibbs Sampling - BUGS Project.