Mathematical Statistics with Applications By K.M. Ramachandran, University of South Florida, Tampa, USA Chris Tsokos, University of South Florida, Tampa, USA Many students who do well in mathematics courses find it difficult to understand the concept of statistics. Mathematical Statistics and Its Applications is unique in that it presents the material with welldefined step by step procedures to solve real problems. This helps the students to approach problem solving in statistics in a logical manner. This textbook provides a calculus based coverage of statistics and introduces students to methods of theoretical statistics and their applications. It assumes no prior knowledge of statistics or probability theory but does require calculus. Most books at this level are written with elaborate coverage of probability. This creates a problem for non statistics majors from various disciplines, who want to obtain a sound background in mathematical statistics and applications. The authors introduce the basic concepts of statistics with sound theoretical explanations. As statistics is basically an interdisciplinary applied subject, many applied examples and relevant exercises from different areas. The book introduces many modern statistical computational and simulation concepts that are not covered in other texts; such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo methods such as the Metropolis algorithm, MetropolisHastings algorithm and the Gibbs sampler. Mathematical Statistics with Applications Contents:  Preface
 Descriptive Statistics
 Basic Concepts from Probability Theory
 Additional Topics in Probability
 Sampling Distributions
 Point Estimation
 Interval Estimation
 Hypothesis Testing
 Linear Regression Models
 Design of Experiments
 Analysis of variance
 Bayesian Estimation and Inference
 Nonparametric tests
 Empirical Methods
 Some issues in statistical applications an overview
 Appendices
