Statistics and Estimation Theory

Course Purpose
The purpose of this lecture course is to understand the concepts of : population, sampling, probability distribution, mean, variance and hypothesis test via Bayesian statistics.
Learning Goals
Students should be able to:
1) understand miscellaneous concepts of Bayesian statistics,
2) estimate the parameters of given probability distributions, and
3) perform hypothesis tests for given statistical problems.
Topic
Session 1Orientation
Basics of statistics
Session 2Descriptive statistics 1 - One-dimensional data
Session 3Descriptive statistics 2 - Two-dimensional data
Session 4Probability and probability distribution (1) - Discrete probability distribution
Session 5Probability and probability distribution (2) - Continuous probability distribution
Session 6Law of large numbers / Central limit theorem
Session 7Fundamentals of inferential statistics
Session 8Confidence interval estimation
Session 9Principle of hypothesis testing
Session 10Test for difference in means
Session 11Analysis of variance
Session 12Non-parametric method
Session 13Design of experiments
Session 14Regression analysis
＊＊This content is based on April 1, 2024. For the latest syllabus information and details, please check the syllabus information inquiry page provided by the university.＊＊