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.
- Step-by-step procedure to solve real problems, making the topic more accessible
- Exercises blend theory and modern applications
- Practical, real-world chapter projects
- Provides an optional section in each chapter on using Minitab, SPSS and SAS commands
- Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods
- Instructor's Manual; Solutions to Selected Problems, data sets, and image bank for students
Table of Contents
Chapter 1. Descriptive Statistics
Chapter 2. Basic Concepts from Probability Theory
Chapter 3. Additional Topics in Probability
Chapter 4. Sampling Distributions
Chapter 5. Statistical Estimation
Chapter 6. Hypothesis Testing
Chapter 7. Goodness-of-Fit Tests Applications
Chapter 8. Linear Regression Models
Chapter 9. Design of Experiments
Chapter 10. Analysis of Variance
Chapter 11. Bayesian Estimation Inference
Chapter 12. Nonparametric Tests
Chapter 13. Empirical Methods
Chapter 14. Some Issues in Statistical Applications: An Overview