Department of Statistics Course Descriptions
 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.