* Statistics in SAS
Studies SAS, the most widely used and powerful statistics
package, for eventual application in regression analysis
and experimental design taught in future courses.
* Statistics in SAS Lab
A course designed to help students understand the SAS
command words, taught in Statistics in SAS (F10272),
through actual practice with computers.
* Statistical Decision Theory
Students will learn to make rational decisions through
statistics, using given information.
* Exploratory Data Analysis
Students learn exploratory analysis techniques that
involve analyzing materials as ¡°they are seen,¡± rather
than using a specific form or method.
* Statistical Mathematics
This course will focus on various mathematical concepts
required for statistics as well as differentiation,
integral calculus, limit function and Taylor¡¯s Theorem.
* Introduction to Bayesian Statistics
Deals with the basic distribution theory, the Bayesian
estimation and verification, and the Bayesian method
of calculation in sequence. Recommended for any student
with a background in basic statistics. The students
also learn the latest trends in statistics such as
using the computer to develop statistical techniques.
* Statistics for Social Science & Lab
This course introduces statistical approaches that
are used in social sciences and enables students to
familiarize themselves with actual material analysis
and interpretation.
* Statistical Computing
Students learn to use the widely used C++ language
and acquire detailed statistical theories through the
use of computer programs. Also surveys statistical
theories through writing application programs that
interface with statistics.
* Statistical Computing Lab
A course designed to help students better understand
the theory taught in Statistical Computing in C through
practice implementations and to enhance their application
skills.
* Probability Distribution Theory
Explains the theoretical background of statistics through
a mathematical approach and enables proper application
of various statistical theories. Focuses on probability,
various probability distributions, and the probability
distribution of a function through a variable conversion.
* Probability Distribution Theory Lab
Enables students to practice what they learned in the
Probability Distribution Theory class, through self-guided
exercises.
* Regression Analysis
Regression analysis is the most fundamental method
of statistical data analysis. Depending on the number
of independent variables, it is categorized into simple
regression analysis and overlapping/double regression
analysis. While this course is primarily concerned
with simple regression, it also deals with the basic
theory of overlapping/double regression
analysis.
* Regression Analysis Lab Students learn how to use real data and
the SAS statistics package to analyze the theory
taught in Regression Analysis 1 and to interpret
the results.
* Mathematical Statistics
This course closely examines the inferential method
in which various parameters comprising each distribution
is inferred based on a variety of probability distributions
and their transforms taught in Probability Distribution
Theory. Aims at establishing a theoretical system since
inference theory and verification theory are the basis
on which all areas of statistics are built.
* Mathematical Statistics Lab
Students learn to apply the content taught in Mathematical
Statistics to real problems and study in detail parts
not covered in that course.
* Time Series Analysis
Analyzes time series data that commonly appear in economics,
business management, and natural science, based on
the Box-Jenkins method. Uses the SAS/ETS statistics
package.
* Time Series Analysis Lab
Students learn to use SAS or their own self-developed
programs to analyze the theory taught in Time Series
Analysis class and to interpret the results.
* Experimental Design
Deals with experiment design plans, data gathering
methods, and statistical techniques of analyzing surveyed
data.
* Experimental Design Lab
What students have learned from lectures on experimental
design theories will be applied to practice using a
relevant package, and afterwards, they will independently
design experiments.
* Survival Analysis
This course deals with statistical inferences on survival
functions, hazard functions, etc.
* Survival Analysis Lab
Students practice the theories they have learned in
the ¡®Survival Analysis¡¯ course using a statistical
package. Through this process, students develop practical
material analysis skills.
* Multivariate Analysis
Deals with the statistical analysis technique that
analyze simultaneously observed data on multi variables.
Main topics include primary property analysis, factor
analysis, and multivariate regression analysis, all
of which are in frequent use.
* Multivariate Analysis Lab
Students gain actual data analysis experience through
practicing with a computer what was learned in the
Multivariate Analysis class.
* Data Mining
The purpose of this lecture is to look at the data
analysis and model selection process to identify information
and knowledge that is useful in the decision-making
process from among vast amounts of data and databases.
* Data Mining Lab
Students will use a statistical package to analyze
actual data in economics and management studies.
* Categorical Data Analysis
Much of observed data falls under the ¡®yes¡¯ or ¡®no¡¯
category. This course studies the statistical techniques
to analyze such categorical data.
* Categorical Data Analysis Lab
Students practice integrating theory and practice by
learning to use SAS statistics package to analyze real
data related to the theory taught in Categorical Data
Analysis.
* Nonparametric Statistics & Lab
A statistical technique that uses data symbols and
sequence as a means of analysis. Used when hypothesizing
a distribution function of a variable is not feasible.
* Spatial
Statistics This lecture will
focus on high-quality graph-based analysis of spatial
statistics. In the case of Geo data, students will
learn how to analyze, based on kriging, block kriging,
and the generally-used variogram and median polish.
In the case of Lattice data, students will learn to
use SAR, CAR, and SMA models.
* Spatial
Statistics Lab Students will use
a statistical package to analyze actual data in environmental,
geographical, and agricultural studies.
* Statistics
for Finance Students familiarize
themselves with statistical methodologies and theories
for financial material analysis.
* Statistics
for Finance Lab Students practice
theories that they learned in the 'Statistics for Finance
course using a statistical package, thereby developing
practical material analysis skills.
* Quality
Control This course deals
with statistical techniques needed for improving and
managing product quality.
* Seminar
in Statistics New themes or special
issues in statistics will be selected and thoroughly
reviewed.
* Statistical
Consulting & Lab Applies to actual
data for analysis the different statistical techniques
acquired from various courses to gain practical experience.
* Statistical
Methodology & Lab This course introduces
the latest research trends in statistics and enables
students to use a wide array of tools.
* Statistical
Survey & Lab Studies scientific
methods of sampling. Deals with statistical data collection
and analysis through actual sampling survey.
* Stochastic
Process Analysis Expands on the
concept of basic probability and discusses new concepts.
Learns about Poisson distribution, Markov Chain, and
a queue.
Seoul Campus 107, Imun-ro, Dongdaemun-gu, Seoul, 130-791,
Korea Tel +82-2-2173-2063 Fax +82-2-2173-3387
Global Campus 81, Oedae-ro, Mohyeon-myeon, Cheoin-gu,Yongin-si,
Gyeonggi-do, 449-791, Korea Tel +82-31-330-4114
Copyright(c)2011. Hankuk University of Foreign Studies. All Rights Reserved.Contact