Mathematical Statistics with Applications in R

1. Descriptive Statistics 2. Basic Concepts from Probability Theory 3. Additional Topics in Probability 4. Sampling Distributions 5. Estimation 6. Properties of Point Estimation, Hypothesis Testing 7. Linear Regression Models 8. Design of Experiments 9. Analysis of variance 10. Bayesian Estimation and Inference 11. Nonparametric tests 12. Empirical Methods 13. Time-series Analysis 14. Overview of Statistical Applications 15. Appendices 16. Selected Solutions to Exercises

Ramachandran, Kandethody M. Kandethody M Ramachandran is a Professor of Mathematics and Statistics at the University of South Florida (USF). His research interests are concentrated in the areas of applied probability and statistics. His research publications span a variety of areas such as control of heavy traffic queues, stochastic delay systems, machine learning methods applied to game theory, finance, cyber security, and other areas, software reliability problems, applications of statistical methods to microarray data analysis, and streaming data analysis. He is also, co-author of three books. He is the founding director of the Interdisciplinary Data Sciences Consortium (IDSC). He is extensively involved in activities to improve statistics and mathematics education. He is a recipient of the Teaching Incentive Program award at the University of South Florida. He is also the PI of 2 million dollar grant from NSF, and a co_PI of 1.4 million grant from HHMI to improve STEM education at USF. Tsokos, Chris P. Chris P. Tsokos is Distinguished University Professor of Mathematics and Statistics at the University of South Florida. Dr. Tsokos' research has extended into a variety of areas, including stochastic systems, statistical models, reliability analysis, ecological systems, operations research, time series, Bayesian analysis, and mathematical and statistical modelling of global warming, both parametric and nonparametric survival analysis, among others. He is the author of more than 400 research publications in these areas, including Random Integral Equations with Applications to Life Sciences and Engineering, Probability Distribution: An Introduction to Probability Theory with Applications, Mainstreams of Finite Mathematics with Applications, Probability with the Essential Analysis, Applied Probability Bayesian Statistical Methods with Applications to Reliability, and Mathematical Statistics with Applications, among others.



Dr. Tsokos is the

"...mathematically rigorous, but takes an applied-student-friendly point of view on the traditional content.highly recommended for use as the primary textbook for an undergraduate mathematical statistics course in a mathematics or statistics department." -- MAA.org