References:

1  Introduction (Probability Theory and Random Variables)  

A First Course in Probability. by Sheldon Ross

2 Data, Sampling and Basic Statistical Inference  

Statistical Inference. by George Casella, Roger L. Berger

For learning more about data-analysis and different practical issues, see this book – which has a lot of case-studies:

The Statistical Sleuth : A Course in Methods of Data Analysis. by F. Ramsey, D. Schafer

Another reference (used for stat 401)

An Introduction to Statistical Methods and Data Analysis by Ott and Longnecker,

3. Regression 

Applied Linear Statistical Models. By Neter, Wasserman, and Kunter

4. Basic concepts in experimental design and ANOVA 

Design and Analysis of Experiments. by Montgomery, DC

Statistics for experimenters: An introduction to design and data analysis. G.E. P. Box, W. G. Hunter, and J. S. Hunter

5. Stochastic Processes  

Introduction to Stochastic Processes. by Paul Gerhard Hoel, Sidney C. Port, Charles J. Stone

6. Elementary Simulation  

Simulation Modeling and Analysis. by Averill M. Law & W. David Kelton

Monte Carlo statistical methods. By Robert, Christian P. and Casella, George

Markov chain Monte Carlo in practice. Gilks, W. R., Richardson, S., and Spiegelhalter, D. J.(ed)

Non-Uniform Random Variate Generation. By Luc Devroye. (This book is free:download from

http://cgm.cs.mcgill.ca/~luc/rnbookindex.html

7. Advanced Inference Techniques

6.1 Randomization tests

6.2 Jack-knife

6.3 Bootstrap