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วิชานี้นำเสนอทฤษฎีและหลักการทางสถิติและความน่าจะเป็นที่จำเป็นสำหรับการวิเคราะห์ทางวิศวกรรมการเงิน โดยเริ่มจากหลักการพื้นฐานของทฤษฎีความน่าจะเป็น (probability theory) ความน่าจะเป็นแบบมีเงื่อนไข (conditional probability) ตัวแปรสุ่มและการแจกแจง (random variables and their distributions) ค่าคาดหมายและโมเมนต์ของตัวแปรสุ่ม (expectation and moments) คุณสมบัติและรูปแบบของการแจกแจงที่ได้รับความนิยมเป็นพิเศษ (special distributions) ทฤษฎีบทสำคัญในทางสถิติ 1) Law of Large Number 2) Central Limit Theorem ซึ่งจะบ่งบอกถึงคุณสมบัติของค่าเฉลี่ยเมื่อกลุ่มตัวอย่างมีขนาดใหญ่มาก การประมาณค่าพารามิเตอร์ด้วยวิธีแบบ maximum likelihood estimation และ Bayes estimation การแจกแจงของตัวประมาณค่า (sampling distributions of estimators) หลักการของการทดสอบสมมุติฐาน (hypothesis testing) และอาจรวมถึงแบบจำลองทางสถิติแบบเชิงเส้น (linear statistical models) วิธีการประมาณค่าแบบ nonparametric
Instructor: รศ. ดร. วีระชาติ กิเลนทอง (tee@riped.utcc.ac.th) และ ดร.สัจจา ดวงชัยอยู่สุข (kei@riped.utcc.ac.th)
Course Schedule: Saturday 9.00 am – 12.00 pm Room 5603
TAs: คุณวาสิณี จันทร์ธร (wasinee_jun@riped.utcc.ac.th ) และ คุณศุภกาญจน์ วงศ์ชัยสุริยะ (karnsupakarnkarn@gmail.com)
1. Course Objective
1. Course Objective
The aim of this course is to give master-level students an introduction to principles, theories, and tools in advanced statistical theory. Students will also learn how to apply statistical models with real 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]
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 12-15 individual assignments during the semester. An assignment with the lowest score will be dropped when calculating the total score for each student. Note: Late submission of the assignments is not accepted; a score of zero will be recorded for such 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 acceptable excuse, a score of zero will be recorded for the examination.
Problem Assignments
1. Problem Assignment 1 (Due on August 28, 2021 at the beginning of the class).
2. Problem Assignment 2 (Due on september 3, 2021 at 11.59 pm).
3. Problem Assignment 3 (Due on september 10, 2021 at 11.59 pm).
4. Problem Assignment 4 and Dataset for Assignment (Due on september 17, 2021 at 11.59 pm).
5. Problem Assignment 5 (Due on september 24, 2021 at 11.59 pm).
6. Problem Assignment 6 and Dataset for Assignment (Due on October 1, 2021 at 11.59 pm).
7. Problem Assignment 7 and Dataset for Assignment (Due on October 15, 2021 at 11.59 pm).
8. Problem Assignment 8 (Due on October 22, 2021 at 11.59 pm).
9. Problem Assignment 9 and Dataset for Assignment (Due on November 5, 2021 at 11.59 pm).
10. Problem Assignment 10 (Due on November 12, 2021 at 11.59 pm).
11. Problem Assignment 11 (Due on November 19, 2021 at 11.59 pm).
12. Problem Assignment 12 (Due on November 26, 2021 at 11.59 pm).
13. Problem Assignment 13 and Dataset for Assignment (Due on December 3, 2021 at 11.59 pm).
14. Problem Assignment 14 (Due on December 10, 2021 at 11.59 pm).
14. Problem Assignment 15 (Due on December 17, 2021 at 11.59 pm).
Course Schedule
The course will be carried out in 15 sessions, totalling 45 lecture hours. The structure of the course is subject to revision if necessary (e.g., to conform to the background, knowledge, and interests of the students). The tentative structure of the whole course is as follows:
Week 1 (August 21, 2021) : Basic Probability Theory. Reading Materials: Lecture Note.
Week 2 (August 28, 2021) : Conditional Probability. Reading Materials: use the same Lecture Note as week 1.
Week 3 (September 4, 2021) : Random Variables and Distributions. Reading Materials: Lecture Note.
Week 4 (September 11, 2021) : Joint Distributions and Conditional Distributions. Reading Materials: use the same Lecture Note as week 3.
Week 5 (September 18, 2021) : Statistical Independence and Distribution of Function of Random Variables. Reading Materials: use the same Lecture Note as week 3.
Week 6 (September 25, 2021) : Expectation and Variance of Radom Variable. Reading Materials: Lecture Note.
October 2, 2021 : MIDTERM EXAM
Week 7 (October 9, 2021) : Covariance, Correlation, and Moments. Reading Materials: use the same Lecture Note as week 6.
Week 8 (October 16, 2021) : Conditional Expectation. Reading Materials: use the same Lecture Note as week 6.
Week 9 (October 30, 2021) : Normal Distributions. Reading Materials: Lecture Note.
Week 10 (November 6, 2021) : Large-Sample Theories. Reading Materials: Lecture Note.
Week 11 (November 13, 2021) : Popular Distributions. Reading Materials: use the same Lecture Note as week 9.
Week 12 (November 20, 2021) : Bayes Estimation. Reading Materials: Lecture Note.
Week 13 (November 27, 2021) : Maximum Likelihood Estimation and Method of Moments. Reading Materials: use the same Lecture Note as week 12.
Week 14 (December 4, 2021) : Hypothesis Testing I.
Week 15 (December 11, 2021): Hypothesis Testing II.
December 18, 2021 : FINAL EXAM
Computer Codes
The course will STATA program as the main statistical tool. Here are slides presentations for the STATA coding tutorials.
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