Tip: You can find the details by clicking the course code or course name
|Credit||Course Code||Title||English Title|
|3||STA1001||통계학입문||INTRODUCTION TO STATISTICS|
|3||STA3103||데이터분석및설계론||DATA ANALYSIS AND DESIGN|
|3||STA3104||범주형자료분석||CATEGORICAL DATA ANALYSIS|
|3||STA3109||수리통계학(2)||MATHEMATICAL STATISTICS II|
|3||STA3110||시계열분석||TIME SERIES ANALYSIS|
|3||STA3112||응용통계학연습||PRACTICE IN APPLIED STATISTICS|
|3||STA3113||응용확률모형론||APPLIED PROBABILITY MODELS|
|3||STA3115||탐색적자료분석||EXPLORATORY DATA ANALYSIS|
|3||STA3117||통계의사결정||STATISTICAL DECISION THEORY|
|3||STA3120||통계적분류방법||STATISTICAL CLASSIFICATION THEORY|
|3||STA3121||6시그마품질경영||SIX SIGMA QUALITY MANAGEMENT|
|3||STA4105||이론통계학연습||SEMINAR IN STATISTICS|
|3||STA4107||전사적품질경영||TOTAL QUALITY MANAGEMENT|
|3||STA4109||통계자료분석||STATISTICAL DATA ANALYSIS|
|3||STA4111||보험통계||STATISTICS FOR INSURANCE|
|3||STA4112||보험통계(2)||STATISTICS FOR INSURANCE(2)|
|3||STA4114||손해보험통계||STATISTICAL MODELS FOR GENERAL INSURANCE|
|3||STA4116||데이터사이언스(1)||DATA SCIENCE I|
|3||STA4117||데이터사이언스(2)||DATA SCIENCE II|
The basic concepts of Statistics in general are introduced systematically at the introductory level.
This course deals with calculus as a basic mathematics necessary for statistics.
This course introduces the concepts and techniques of matrix algebra and explains their statistical applications. This course covers determinants, inverse matrix, eigenvalues and eigenvectors, and singular-value decomposition.
Students learn computer languages and data processing mainly used in Statistics through practical exercises.
Students are expected to understand the basic concepts of Statistics and learn how to process real data using statistical packages such as SAS, SPSS, MINITAB, and R.
This course deals with statistical analysis, analysis and analysis of economic phenomena. For example, it covers how to collect and organize population and production statistics to measure and analyze the labor force and productivity, which are symbols of economic activity Analyzing the income and expenditure of general urban workers households, analyzing the economic feasibility, examining the method of grasping the price, and discussing the method of making and analyzing the national income account
Statistical theories used for multivariate data analysis are described. It mainly deals with principal component analysis, factor analysis, discriminant analysis, and cluster analysis.
Discuss and practice all the processes of statistical research project from designing social survey, preparing questionnaire, collecting data, inputting and analyzing data, making report, presentation.
This course deals with the analysis of 2-Dimentional contingency table and multi-dimensional contingency table. Specifically, it conducts test of significance in 2 2 contingency table, chi-square test in IJ contingency table, matching degree of nominal data, kappa as the matching degree, matching degree of ranked data, Linear Algebraic model, estimation and verification of model in 3 - D contingency table, optimal model selection in Linear Algebraic model, logit model and logistic regression model.
This course covers Bayesian theorem, posterior distribution, prior distribution, extreme posterior distribution, Bayesian estimation and testing, secondary decision theory, and Bayesian decision theory.
Aside from the parametric test procedure where the distribution of the population is assumed, leading to test statistics distributing with t-distribution, F-distribution, or chi-square distribution, this course covers the statistical test procedure that does not assume a specific distribution of the population. Particularly, it examines the sign based test based on binomial distribution, the test of the percentile, McNemar test, and the test for the contingency table using the chi square test. Rank-based tests include Mann-Whitney test and Kruskal-Wallis Test, as well as Cochran's Q test, Quade test, Friedman test, and Kolmogorov-Smirnov tests. We also discuss the comparison with the parametric test.
We introduce statistical techniques to find risk factors that affect the survival time by using regression model about survival time and estimating and testing survival time. This course deals with estimation of the survival function, Kaplan-Meier estimation, and proportional risk model of Cox and accelerated regression models.
This course deals with limiting distribution including the central limit theorem, estimation, statistical hypothesis, hypothesis testing, test using non parametric methods, the statistics required for this test, sufficiency, theories required to understand statistical inference.
Students learn statistical methods to analyze time series data. The identification, estimation, testing and forecasting of the ARIMA model is covered. This course also covers the basics of multivariate time series analysis.
Students will learn about the basic theory of various experimental design methods such as one-way design, two-way design, factorial design, response surface analysis, conduct analysis using computer.
This course takes more in-depth approach to topics that were covered in lower grades and covers the emerging topics in Applied Statistics.
This course deals with various probability models based on basic probability concepts and tries to apply them in practical cases.
Students learn theoretical approaches to Statistics based on Mathematical Statistics.
An Exploratory Data Analysis (EDA) technique to identify the structure and characteristics of data is introduced. Specifically, we introduce graphical methods for transforming data into stem-leaf and box data, scatter plot, smoothing technique, median polish, and graphical methods for multivariate data.
Students learn how to make economic and managerial decision making based on basic Statistics. Non-probability related decision making method such as Minimax, Maximin, and decision making methods using Bayesian statistics are covered.
The objective of this course is to familiarize students with the technical and practical aspects of data mining, which is a methodology for converting customer's hidden information to knowledge based on overall understanding of CRM and technology (solution comparison).
Statistical methods of classifying new entities and comprehending them with the attributes of the groups in which the entities are classified into are dealt in this course. This course deals with corporate default prediction model and customer credit rating model as practical application examples.
This course deals with statistical technique required for quality control, including sampling test, control chart and reliability analysis method for estimating the product life.
This course covers statistical theories such as sample and sample distribution, interval estimation, hypothesis testing, and nonparametric methods, and students learn how to apply them to real data.
This course introduces various theories about survey method, sampling method to conduct market research, opinion survey or academic research. In addition, survey planning method, sample extraction method, estimation method of parameters and errors are introduced, along with actual examples.
This course deals with theories on probabilistic phenomenon such as conditional probability, concept of stochastic process, random walk, Markov chain, features and application of the Markov chain, stopping time, basic concept of Poisson process, Brownian motion, diffusion process and Martingales.
We learn basic theory about simple linear regression model, multi variate linear regression model, regression diagnosis, selection of explanatory variable, and analyze actual data using statistical package.
This course introduces concepts of probability, probabilistic thinking, and probability Models, which form the basis of Statistics. Expectation, moment generating function, and probability distribution theory including conditional distribution theory and sample distribution is also covered.
Students learn theoretical background of numerical analysis and comprehend and practice efficient programming of statistical methodology.
This course deals with the analysis of financial theory, including financial investment, financial risk, financial profit, and financial loss, using probability theory and Statistics.
The purpose of this study is to learn how to extract meaningful information from large amount of data and to study the applicable case.
This course takes an in-depth look at statistics or probabilities theories and recently developed statistical methods that are not covered in the undergraduate courses.
This course takes a more detailed approach to topics that were dealt lower grades, and also covers the emerging topics in the field of theoretical statistics.
This course deals with statistical analysis of various phenomena related to population. Students comprehend the demographic structure, demographic process of death, birth, marriage and mobility, and the conditions and forecasting methods for setting various population models.
This course deals with leadership, strategic planning, information system, human resource management, customer satisfaction, process management, and management performance evaluation as an introduction to quality management and environmental management which are important in modern corporate management.
This course deals with the application of statistical estimation and test, correlation and regression analysis, variance analysis, contingency table analysis, and basic multivariate analysis to actual data. This course also deals with statistical simulation methodology.
Students learn how to analyze real data using statistical packages such as SAS, SPSS, BMDP, and MINITAB. The emphasis is on the composition of the problem, the assumptions of the statistical model, and the analysis of the results rather than the use of the statistical package itself.
This course covers survival distribution, life tables, life insurance, interest theory, basic pensions and general pensions, survivor pensions, and net premiums.
This course is an advanced course of statistics for insurance and takes in-depth approach to theories applied in field of insurance.