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.

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.