Statistics 328 Texts, References, Syllabus, and Grading
Required Text
W. N. Venables and B. D. Ripley: Modern Applied Statistics with
S (4th ed.), Springer 2002
Other References
Introduction to R
(included when you download the R system) and copies available
Syllabus
The areas and the depth of the coverage will be adjusted - according
to the objectives of the students in the group.
The general objective is: to acquire an orientation in major areas of
statisticalcomputing by hands-on experience.
- Splus and R
- How to start, quit and get help
- Stuctures: lists, vectors, arrays
- Graphics
- Operations: artithmetical and statistical
- Programming: functions, loops, program control
- Generating Random Data
- Discrete and continuous distributions
- Simulating probability models
- Central Limit Theorem
- Confidence intervals and coverage probability
- Regression and Numerical Methods
- Linear regression and matrix operations
- Nonlinear regression, neural networks, and optimization
- Smoothing
- Density Estimation
- Scatter plot smoothing
- Smoothing Splines
- Monte Carlo
- Monte Carlo Integration: laws of large numbers
- The Bootstrap and jacknife
- Advanced topics: Markov Chain Monte Carlo
- Applications: simulations of queueing systems, statistical procedures
- Other topics: Classification and Regression Tress
Grading
- Homework assignments: 50%
- In-class/Take-home midterm exam: 25%
- Final project: 25%
Check out your grades on the
Campus GradeBook
!