[Department of Industrial Systems Engineering]
IE231 Applied Real Analysis and Probability (응용해석 및 확률분석)
This course introduces some basic concepts in mathematical analysis for engineering applications. Topological ideas in ﬁnite dimensional Euclidean spaces, deﬁnition and properties of upper and lower limits of sequences of real numbers, continuous functions are investigated. Then diﬀerentiation and Riemann-Stieltjes integration are introduced. (uniform) convergences in function spaces are covered. We also discuss applications of mathematical analysis to the derivation of some limit theorems as well as the study of convexity and convex optimization.
IE241 Engineering Statistics I (공학통계 I)
To be added
IE341 Engineering Statistics II (공학통계 II)
This course is a sequel to Engineering Statistics I and deals with statistical estimation, hypothesis testing and additional topics.
IE342 Regression Analysis and Experimental Designs(회귀분석 및 실험계획법)
This course is concerned with optimal design of products, processes, or softwares using experimental design methods. Major topics include: Design and analysis of experiments (DOE) for robust product/process/software design (e.g., Taguchi Methods). Case studies from various industrial sectors will be discussed.
IE442 Statistical Data Analysis (통계자료분석 사례연구)
Statistical analysis and mining of complex data in the areas of engineering and business management. Topics include graphical data analysis techniques, regression, decision tree analysis, clustering analysis, artificial neural networks, etc.
IE542 회귀분석의 이론과 실제
To be added
To be added
IE643 Design and Analysis of Experiment (실험계획 및 분석)
This course introduces basic principles of experimental design and data analysis methods. Topics include: Principles of experimental design, Analysis of variance, Types of experimental designs and analysis methods, Systems modeling & optimization using Response Surface Methodology and Taguchi Method, and case studies.
[Department of Mathematical Sciences]
MAS250 Probability and Statistics (확률및통계)
This course introduces basic notions and methods of Statistics. Basic theory of probability is also introduced with plenty of examples. Independence of events and random variables, and various probability distributions, such as Poisson, exponential and Gamma, are treated. Then expectation, conditional expectation, law of large numbers, parameter estimation, simple linear regression, and one-way ANOVA are covered among others.
MAS350 Elementary Probability Theory (기초확률론)
To be added
MAS550 Probability Theory (확률론)
This is a graduate level course on probability theory. This course covers important concepts and key theorems of probability theory.
CC511 Probability and Statistics (확률및통계학)
An introductory level of statistics and probability will be lectured tuned to the graduate students from a variety of disciplines. The textbook contains a good number of examples from real experiments which may help students doing their own research works. Some important topics include
MAS582 Topics in Mathematics_Survey sampling (수학특론_표본조사론)
1. Random variables and their distributions
2. Descriptive statistics
3. Statistical estimation and sampling distribution
4. Inferences on population means
5. Discrete data analysis
6. ANOVA and simple linear regression.
Focus on aspects and basic theory of design and estimation in sample surveys for finite populations. Basic concepts of probability sampling. Simple random, systematic, stratified, cluster multi-stage and unequal probability sampling. Horvitz-Thompson estimation of totals, means, proportions, regression coefficients. Model-assisted ratio and regression estimation. Linearization technique for variance estimation. Replication method for variance estimation. Two- phase sampling and sampling on two occasions. Non-response effects. Imputation.
[School of Buesiness and Technology Management]
MSB201 Statistical Analysis for Business (통계분석론)
This course discusses some statistical analysis tools in undergraduate levels of business and economics. This course assumes that students already have taken an introductory statistics course. The course develops methods for analyzing statistical relationships. Techniques studied in the course are useful for a variety of business applications in accounting, finance, marketing, production and others areas. The course emphasizes formulating models and using them for decision-making prediction. Topics include regression analysis, analysis of variance, goodness-of-fit test, time series analysis, sampling methods, some statistical decision theory, and nonparametric. And some multivariate techniques can be discussed, if time permits. The course involves extensive hands-on work with SPSS or other packages. For all the issues, both theoretical and practical aspects through case studies will be emphasized. After completing this course, students are expected to make practical use of modern data analysis, and appreciate the need for, and limitations of, real world data.
MSB301 Econometrics (계량경제학)
이 과목은 통계학과 경영경제수학을 수강한 2-3학년 학생을 대상으로 개설된 과목임. 오늘날, 계량경제학은 거의 모든 사회과학분야의 분석에 필요한 첨단 분석기법을 제공하는 분야이다. 이 과목에서는 계량분석에 필요한 기본적인 분석기법을 학생들에게 소개할 것이다. 주요 주제에는 다중회귀분석, 회귀분석과 관련된 문제점들에 대한 검토, 시계열분석 등이 포함될 것이다. 통계학과 경영경제수학을 수강한 학생만이 수강할 수 있다.
MSB500 Advanced Statistics for Management (고급 경영통계)
이 과목은 석박사과정의 경영학을 전공하는 학생이 필요한 통계학적 방법론을 공부하며, 수강학생들이 확률통계학 기본지식을 가지고 있다고 전제한다. 특히 경영의사결정에 필요한 확률이론, 표본, 추정과 검정,회귀분석, 분산분석, 적합도 검정, 요인분석 등 통계적 의사결정 등에 관한 이론과 실제 사례를 중심으로 공부한다.
MSB601 Research Methodology I (연구방법론 I)
This is an introductory graduate level seminar on research methods in business. Together, we will examine a variety of issues on research methods including research design, experiments, quasi-experiments, survey development, qualitative research methods, and others. I want this seminar to be explorative and thought-provoking mutual learning experiences by active engagements of all members of the class. Your engagement and contribution should be based on your thorough reading, comprehensive understanding of the materials, and imaginative thoughts on a variety of business problems.
MSB701 Advanced Econometrics (고급계량경제학)
This is an Advanced level graduate course in Applied Econometrics. Topics to be studied include specification, estimation, and inference in the context of models that include then extend beyond the standard linear multiple regression frameworks.
MSB702 Research Methodology II (연구방법론 II)
After a review of the linear model, we will develop the asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models. We will then turn to instrumental variables, maximum likelihood, GMM, and two step estimation methods.
Inference techniques will be extended to include Wald, Lagrange multiplier and likelihood ratio and tests for nonnested hypotheses such as Hausman specification test and Davidson and MacKinnon’s J test. Modelling frameworks will include the linear regression model and extensions to models for panel data, multiple equation models, and models for discrete choice.
In addition to these, class will cover some important published articles in financial and monetary economics..
The main purpose of this course is to study advanced level of research methodology for empirical analysis. After introducing experimental design focusing on validity, the course covers methodologies for multivariate data analysis such as ANOVA, Factor Analysis, Regression, Discriminant Analysis, Conjoint Analysis, MDS, etc,.