Mathematical Statistics with Applications provides a calculus-based theoretical introduction to mathematical statistics while emphasizing interdisciplinary applications as well as exposure to modern statistical computational and simulation concepts that are not covered in other textbooks. Includes the Jackknife, Bootstrap methods, the EM algorithms and Markov chain Monte Carlo methods. Prior probability or statistics knowledge is not required.
Chapter 1 Descriptive Statistics
Chapter 2 Basic Concepts from Probability Theory
Chapter 3 Additional Topic in Probability
Chapter 4 Sampling Distributions
Chapter 5 Point Estimation
Chapter 6 Interval Estimation
Chapter 7 Hypothesis Testing
Chapter 8 Linear Regression Models
Chapter 9 Design of Experiments
Chapter 10 Analysis of Variance
etc.