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Instructor: รศ. ดร.วีระชาติ กิเลนทอง (tee@riped.utcc.ac.th)
Course Schedule: Saturday 9.00 am – 12.00 pm Room 5602
TAs: ดร.สัจจา ดวงชัยอยู่สุข (kei@riped.utcc.ac.th)
This course studies basic probability and statistical theory concepts relevant to financial engineering. The topics include basic probability, conditional probability, random variables and their distributions, expectation and moments, special distributions, asymptotic theory and properties of large random samples, point estimation and maximum likelihood estimation, sampling distributions of estimators, hypothesis testing, linear statistical models, and basic nonparametric methods.
1. Course Objective
1. Course Objective
This course aims to introduce master-level students to principles, theories, and tools in basic statistical theory. Students will also learn how to apply statistical models with actual data using STATA software.
3. Required Textbooks:
3. Required Textbooks:
1. DeGroot, Morris H. and Mark J. Schervish. 2012. Probability and Statistics. 4th edition: Preason. [DS]
2. Hogg, Robert V., Allen T. Craig and Joseph W. McKean. 2005. Introduction to Mathematical Statistics. 6th edition, Pearson. [HCM]
3. Wooldridge, F.M. (2020). Introductory Econometrics: A Modern Approach (7th Edition). CENGAGE. [W]
Optional Textbooks:
Ross, S. M. (2014). Introduction to Probability Models. Academic press.
Data Sources
We will provide relevant data through the course website: https://sites.google.com/riped.org/tee/teaching/statistics
Program Sources
4. Grades and Requirements
4. Grades and Requirements
Grades will be based on the following weights:
30% Assignment(s)
30% Mid-Term Exam
40% Final Exam
Tentative Grading Range:
85 – 100 A
80 – 84 B+
70 – 79 B
65 – 69 C+
55 – 64 C
50 – 54 D+
40 – 49 D
39 or less F
4.1. Assignment
4.1. Assignment
Students will be assigned to complete 14 individual assignments during the semester. An assignment with the lowest score will be dropped when the total score for each student is calculated. Note: Late submission of the assignments is not accepted; a score of zero will be recorded for that assignment.
4.2. Examination
4.2. Examination
There will be two examinations: a mid-term exam counting for 30% of the total points and a final exam counting for 40% of the total points. If a student misses a regular examination without an acceptable excuse, a score of zero will be recorded for the examination.
Course Schedule
The course will be in 15 sessions, totaling 45 lecture hours. If necessary, the course structure is subject to revision (e.g., to conform to the student's background, knowledge, and interests). The tentative schedule of the whole course is as follows:
Week 1 (August 15, 2026) : Basic Probability Theory. Lecture Note; STATA Code; Data.
Week 2 (August 22, 2026) : Conditional Probability. Use the same Lecture Note as Week 1.
Week 3 (August 29, 2026) : Random Variables and Their Probability Distributions.
Week 4 (September 5, 2026) : Joint Distributions and Conditional Distributions.
Week 5 (September 12, 2026) : Statistical Independence and Distribution of Function of Random Variables.
Week 6 (September 19, 2026) : Expectation and Variance of Random Variable.
September 26, 2026 : MIDTERM EXAM (9.00 am to 12.00 pm) Covering Week 1-5
Week 7 (October 3, 2026) : Covariance, Correlation, and Moments.
Week 8 (October 10, 2026) : Normal Distributions and Popular Distributions.
Week 9 (October 17, 2026) : Conditional Expectation.
Week 10 (October 24, 2026) : Large-Sample Theories.
Week 11 (October 31, 2026) : Point Estimation: Bayes and MLE Estimations.
Week 12 (November 7, 2026) : Hypothesis Testing
Week 13 (November 14, 2026) : Regression Model: Estimation
Week 14 (November 21, 2026) : Regression Model: Inference
November 28, 2026 : Final EXAM (9.00 am to 12.00 pm)
Problem Assignments
1. Problem Assignment 1 (Due on August 22, 2026, at the beginning of the class).
Computer Codes
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