For more details on the courses, please refer to the Course Catalog

Classification | Course Code | Course Title | Credit | Language of Instruction | Course Availability |
---|---|---|---|---|---|

Undergraduate | STA2008 | Mathematics for Statistics | 3 | Korean | Yes |

Basic mathematical tools needed to learn statistical theories. This course includes basic manipulation of vectors and matrices, eigenvectors. Existing methods solve typical linear equations, etc. This course also will include differentiation and integration. Especially, partial differentiation, several rules useful in differentiation, and multiple integration methods, and special functions. | |||||

Undergraduate | STA2009 | Introduction to Statistical Computing | 3 | - | No |

This course introduces students to a range of computational techniques that are important to statistics. The topics covered include numerical linear algebra, numerical optimization, graphical techniques, numerical approximations, numerical integration and Monte Carlo methods. Use of statistical packages (R, SAS) and programming libraries is also illustrated. | |||||

Undergraduate | STA2010 | Introduction to Regression Analysis | 3 | English | Yes |

Introduction to basics of linear regression models. Topics covered include simple linear regression, ordinary least squares, the geometry of least squares, F tests and ANOVA table, residuals, outlier detection, and identification of influential observations, etc. | |||||

Undergraduate | STA2011 | Statistics | 3 | Korean | Yes |

Introduction to statistical concepts and methods for the collection, presentation, analysis, and interpretation of data. Histograms, means, standard deviations, medians as descriptive and summary statistics, and several important distributions including binomial and normal distributions. This course will concentrate on statistical inferences based on the knowledge from Introduction to Statistics. Basic concepts of estimation, testing and, test efficiency. Introduction to regression and analysis of variance will be covered | |||||

Undergraduate | STA2014 | Introduction to Mathematical Statistics | 3 | Korean,English | Yes |

Topics include the concept of random variable and several statistical probability functions. Characteristics and relationships among the functions will be studied to apply to real world. Also random sample and distribution of sample mean will be explained | |||||

Undergraduate | STA2016 | Introduction to statistical programming | 3 | Korean,English | Yes |

This course introduces basic logic and grammars for computer programming based on the statistical work and programming environment R which is the most widely used statistical language for professional statisticians. The first part of the course gives an introduction to R. In this stage, students learns how to assign variables, to import and export data, to handle datatypes, to use loop statements, and to write user-defined functions. In the second part, this course provides a brief review for some basic statistical theory and methods to compute basic statistics. In addition, students learn some important numerical methods for optimization, differentiation, and integration. | |||||

Undergraduate | STA2017 | Matrix algebra for statistics | 3 | Korean,English | Yes |

This course is a basic course for undergraduate students in statistics to provide useful and practical theory in matrix and linear algebra. We will start with basic operations such as addition, multiplication, trace and transpose of matrices and will discuss how to calculate determinants and inverse of matrix. We will further understand matrix algebra by introducing axiomatic linear algebra theory. For intermediate topics, we will introduce differentiation of matrix and matrix decomposition. Such basic knowledge will be applied to derive basic probability distributions. Finally, multiple regression will be illustrated by matrix algebra. | |||||

Undergraduate | STA3001 | Introduction to Time Series Analysis | 3 | English | Yes |

Basic concepts of time series are presented, including stationarity, causality, invertibility, autoregressive moving average models, and forecasting. Methods for building AR, MA and ARMA models are discussed. The course also introduces ARIMA models, and briefly touches on state space models and the Kalman filter. Real-world data are used as illustrative examples. | |||||

Undergraduate | STA3003 | Introduction to Statistical Inferences | 3 | Korean,English | Yes |

Introduction to basic concepts and idea of estimation and hypotheses testing which are necessary to statistical decision making. Emphasis on point estimations, interval estimations and classical test procedures and practice with real examples will be provided. | |||||

Undergraduate | STA3005 | Introduction to Bayesian Statistics | 3 | English | Yes |

This course is an introduction to practical Bayesian methodology. The use of conjugate families, introduced in Intermediate Statistics is discussed. The emphasis throughout is on the application of Bayesian thinking to problems in data analysis. | |||||

Undergraduate | STA3007 | Statistical Simulation | 3 | English | Yes |

Generation of uniform and nonuniform random numbers, discrete event simulations, simulation languages, design of simulations, statistical analysis of the output of simulations, applications to modeling stochastic systems in computer science, engineering, and operations research. | |||||

Undergraduate | STA3008 | Introduction to Multivariate Statistical Analysis | 3 | English | Yes |

Introduction to multivariate statistical analysis; statistical methods for analyzing and displaying multivariate data: dynamic graphics, principal components, factor analysis, canonical correlations, cluster analysis, classification methods, Hotelling's T2, multivariate analysis of variance. | |||||

Undergraduate | STA3010 | Introduction to Nonparametric Statistics | 3 | English | Yes |

Introduction to nonparametric problems; tests based upon sample distribution functions, rank tests for location, scale and independence; local properties of rank tests. | |||||

Undergraduate | STA3011 | Introduction to Biostatistics | 3 | - | No |

Statistical methods useful for biostatistical problems. Topics include analysis of observational studies and randomized clinical trials, techniques in the analysis of survival and longitudinal data, approaches to handling missing data, and meta-analysis. Examples will come from recent studies in cancer, AIDS, heart disease and psychiatry and from studies to evaluate health care. | |||||

Undergraduate | STA3012 | Categorical Data Analysis | 3 | English | Yes |

The objective is to provide theory and methods for analysis of cross-classified categorical data. The main subject areas are descriptive and inferential statistics for two-way contingency tables, logistic regression, loglinear and logit methods for multidimensional tables, models for ordinal categorical data, and maximum likelihood and asymptotic theory for models for categorical variables. |