Instructor: Jimin Ding;
Office: Cupples I, Room 112A;
Email: jmding@math.wustl.edu
TA/Grader: Adam Cheng (email: adam.cheng@wustl.edu)
Office Hours: Wed. 3:00-4pm. Friday 1-2pm. or by appointment
Topics covered:
Mathematical theory and application of probability at the advanced undergraduate level; a calculus based introduction to probability theory. Topics include the computational basics of probability theory, combinatorial methods, conditional probability including Bayes´ theorem, random variables and distributions, expectations and moments, the classical distributions, the law of large numbers, and the central limit theorem.
Math 493 is now the prerequisite of Math 494, which will be offered every spring. Math 493 is also cross listed as ESE 428.
Prerequisites:
The prerequisite is Math 318 or Math 308 , or equivalent mathematical maturity and experience (permission of instructor).
Textbook:
Robert Hogg, Joseph McKean and Allen Craig,
Introduction to Mathematical Statistics, 7th edition
Pearson Prentice Hall, 2012, ISBN 978-0-13-008507-8
eTextbook ISBN: 978-0-321-79543-4
Remarks:
- Solutions to selected problems are in the back of the book.
- We will cover selected topics from Chapters 1-3 and 5. Detailed schedule will be updated periodically on the course website.
- The same book will be used for Math 494 in the coming Spring.
- The 7th edition of the textbook is different from the order (the 6th) edition. The major changes are in chapter 4, which contains the old chapter 4 and the basic statistical inferences from the old chapter 5, and chapter 10, which contains old chapter 10 and the dicussion of robustness concepts from the old chapter 12. Our homework assignments will be based on the 7th edition.
- Course reserved desk copies of the textbook (both 6th and 7th edition) and reference books are available in Olin library for 2 day use.
Reference Books:
- Statistical Inference, 2nd ed., George Casella and Roger L. Berger,
Duxbury Thomson Learning, Learning, 2002.
- All of Statistics: A Concise Course in Statistical Inference, Larry Wasserman, Springer, 2004.
- Applied multivariate statistical analysis, 6th ed., Richard A. Johnson, Dean W. Wichern., Prentice Hall, 2007.
Exams and Homeworks:
There will be two in-class midterms on Oct. 5 (Fri.) and Nov. 9 (Fri.) respectively and a final exam at 3:30-5:30pm. on Dec. 17 (Mon.). Midterms are not accumulative, but the final exam is comprehensive. All exams are closed book and closed notes, and you may need a calculator for them.
For each exam, at least 60% of the questions will be similar with, if not exactly same as, the homework questions, and 30% will be close to the examples discussed in class.
There will be about 2 homework questions every lecture and homework will be collected every Friday in class. No late homework is accepted.
Grades:
Your grade will be based on 2 in-class midterm exams and one final exam, together with weekly homeworks in the proportions. The lowest homework grade will be dropped automatically. Then your final letter grade is determined as follows. The A range will be 85 to 100, the B range will be 75 to 85, the C range will be 65 to 75, and the D range will be 60 to 65, with plus and minus grades given to the top 10% and bottom 10% students in each of these ranges. (If you elect ``Credit/No Credit'', Cr means D or better.)
Midterm exams |
20% each |
Final exam |
30% |
Homework |
30% |
Collaboration:
Collaboration on homework is allowed and can be helpful (and fun). However, you must do all written work by yourself, both answers to homework questions and computer programs. If you collaborate with someone on a homework, list his or her
name in a note at the top of the first part of your homework.
There should be NO COLLABORATION on exams.
Good books for reviewing elementary statistics:
- A Data-Based Approach to Statistics,R. L. Iman,
Duxbury Press, 1994.
- Statistics and Data Analysis
from Elementary to Intermediate, A. J. Tamhane and D. D. Dunlop, Prentice-Hall, 2000.
- Design and analysis of experiments, 2nd ed., Douglas
Montgomery, John Wiley & Sons, 1984. (Good for multiple-comparison
procedures.)
- Applied Linear Statistical Models, 4th ed., John Neter,
M. Kutner, C. J. Nachtsheim, and W. Wasserman, Irwin/McGraw Hill, 1999.
- Applied Multivariate Statistical Analysis. 5th ed., R.
A. Johnson and D. W. Wichern, Prentice Hall, 2002.