Mathematical Statistics with Applications, Second Edition, gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner. 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 (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. Goodness of fit methods are included to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Engineering students, especially, will find these methods to be very important in their studies.
Descriptive Statistics
Basic Concepts from Probability Theory
Additional Topics in Probability
Sampling Distributions
Estimation
Properties of Point Estimation, Hypothesis Testing
Linear Regression Models
Design of Experiments
Analysis of variance
Bayesian Estimation and Inference
Nonparametric tests
Empirical Methods
Time-series Analysis
Overview of Statistical Applications
Appendices
Selected Solutions to Exercises