COLLEGE OF MANAGEMENT
Department of Statistics
Degrees Offered:B.A., M.B.A.
TO Webpage:
Chair:Tsai, Tzong-ru
The Department
The forerunner of the Department of Statistics was the Statistics Section in the Department of Accounting and Statistics founded in 1963. The Department of Statistics was organized as an independent department in 1973. The master's program was established in 1997. Since 1963, over 4,000 Bachelor's degrees and Master's degrees have been granted.
The Department offers broad undergraduate and graduate programs to fulfill many needs of students at different levels. Both programs give students sufficient flexibility to pursue their special interests and time to take courses in other departments. At undergraduate level, there are several introductory courses which lead to many more advanced courses that are designed to provide students with understanding of the concepts of statistical inference and familiarity with the methods of applied statistical analysis. The Department's master program stresses a balance between statistical theory and practical applications, preparing students for careers in industry, business, government, medical research, and academia. Both undergraduate and graduate programs cultivate students' abilities to do data analysis of real world problems in diverse areas.
The Department mainly emphasizes the practice of sample survey, marketing analysis, industrial engineering, biological sciences, and many other areas. To accomplish the Triple-Objective of the University and multimedia-aided instruction, all faculty members are encouraged to make multimedia-aided teaching materials for the required courses. By merging the interest and expertise of the faculty with campus information network, we encourage faculty and graduate students to engage in cooperative research with people in other areas. To adjust to the age of the knowledge economy, promote competitive capability, meet the demands of industry, offer opportunities for in-service people, and train students to be statistical specialists both in theory and practice, the Department, in collaboration with the Graduate Institute of Management Sciences, offers a Ph.D. degree in Management Sciences with emphasis in Statistics.
Faculty
Professors
Chang, Chun-tao ; Lin, Kuang-nan ; Tsai, Tzong-ru ; Wu, Chin-chuan ; Wu, Shu-fei ; Wu, Shuo-jye
Associate Professors
Chen, Ching-hsiang ; Deng, Wen-shuenn ; Lee, Hsiu-mei ; Lin, Jyh-jiuan ; Wen, Bor-shyh
Assistant Professors
Chen , Li-ching ; Chen, Yi-ju ; Li, Pai-ling
Lecturers
Wang, Wen-yen; Yang, Wen
Degree Requirements
The Department of Statistics offers both undergraduate and graduate programs.
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Requirements for a degree of B.A. in Statistics
Successful completion of 139 credits of courses, including 109 credits of required courses and 20 credits of elective statistics courses. - Requirements for a degree of M.B.A. in Statistics
A master's degree requires a minimum of 36 graduate credits including 9 credits of required courses, 4 credits of Topics in Applied Statistics and 4 credits of Seminar. At least 30 credits must be completed within the Department. Student are required to complete a thesis under the supervision of a faculty member of the Department, submit a thesis, and pass an oral defense on the thesis.
Course Descriptions
Undergraduate Courses
B0032 Marketing Survey (0/3) This course is an introduction to scientific research skills for managers, research processes and designs, measurement and selection, and data collection.
B0106 Actuarial Analysis (2/2) This course covers the theory of interest, concepts of certain annuities, measurement of mortality and life table, life annuities, life insurance, net annual premium, net level premium reserves, pricing for casualty insurance, statistical base, overall average pure premium and/or loss ratio, construction of loading and gross premium, risk classification, and ration plan.
B0109 Insurance (0/3) This course is what insurance is all about: risk management and insurance, insurance and the law, insurance contracts policy analysis, limitation of amount of recovery, loss-adjustment provisions.
B0124 Econometrics (2/2) Econometrics is designed to give students an understanding of why econometrics is necessary, and to provide for them a working ability with basic econometric tools.
B0263 Money and Banking (2/2) This course deals with the nature and functions of money and finance, commercial banking, central banking, monetary theory, and international monetary relations.
B0302 Economics (3/3) This course discusses two main topics: individual economy includes price and theory of supply and demand, analysis of acts consumers, theory of production, structure of cost, structure of market, and supply and demand of production factors; and collective economy as the study of national income, determining rules for equalization of income standard.
B0456 Security Investment Analysis (2/2) This course focuses on equities analysis of investment. Students can learn how to invest securities in stock markets. Moreover, the course emphasizes reasonable prices of companies, including fundamental analysis, technical analysis, when and how to buy and sell by investors, how to set up investment framework of top-to-down.
E1034 Introduction to Information Science (2/2) Topics of this course cover: 1. Introduction to information society; 2. Networking operations and applications; 3. Word processing; 4. Hardware of computers; 5. Software of computers; 6. Applications of computer; 7. Computer programming.
M0115 Multivariate Analysis (0/3) This course covers review of matrix theory, univariate and multivariate normal distributions. Inference about multivariate means including Hotelling's T squared. Inference about covariance structure including principal components, factor analysis, and canonical correlation. Classification techniques including discriminant and cluster analysis.
M0153 Operations Research (2/2) This course includes basic techniques for modeling and optimizing deterministic systems and stochastic models with emphasis on linear programming, integer programming, queuing theory, and inventory. Applications to production, logistics, and service systems are also covered.
M0191 Survey Sampling (3/0) This course is an introduction to the design of sample surveys and the analysis of survey data, the course emphasizes practical applications of survey methodology. Topics include sources of errors in surveys, questionnaire construction, simple random, stratified, systematic and cluster sampling.
M0202 Quality Control (2/2) This course covers introduction to statistically based quality improvement methods useful in industrial settings, inspection data for quality control, sampling plans for acceptance inspection, and charts for process control.
M0203 Case Studies in Government Statistics (2/2) This course covers introduction to the organization and major responsibilities of Government Statistics Affairs, and focuses on the training of case studies. This course also emphasizes the needs of sitting for Civil Service Examination.
M0264 Time Series (0/3) This course covers autocorrelation and elements of spectral analysis, auto-regressive and moving average models, identification and fitting, forecasting, and seasonal adjustment.
M0298 Seminar on Statistics I (2/2) This course includes special topics in probability theory and mathematical statistics designed to meet the needs and interests of individual students.
M0339 Accounting I (3/3) The course focuses on the accounting concept, the accounting model, and the relationship of the financial statements, otherwise, the course also discusses about the accounting of single proprietorship, partnership, and corporation.
M0344 Data Processing (2/2) This course aims to introduce students to use computer to administrate, process and manage mass and complex data.
M0364 Computer Applications in Statistics (2/2) This course covers organization and application of computers and statistical packages to data processing. Other topics also include data handling in terms of coding, preparation, acquisition, file organization and retrieval, screening and reduction, summarization and tabulation, and statistical analysis, and survey of available packages and applications.
M0404 Management Mathematics (3/3) This course introduces several mathematical models and methods for various real world situations that may be encountered in the applications of management sciences. Emphasis of the course is on its applications.
M0405 Management (3/0) The course offers students not only theoretical frameworks that guide managerial activities but also illustrations and examples of how and when those theories do and do not work in both small and large businesses as well as in nonprofit organizations.
M0481 Applied Categorical Data Analysis (0/3) This course covers methods of analyzing multidimensional contingency tables with an emphasis on practical applications. Topics cover the use of computing packages for analysis of such data, model selection, testing goodness of fit, and estimation of parameters.
M0517 Statistics (4/4) This course covers graphical and numerical descriptive measures, probability, random variables, expectations and variances, sampling distributions, central limit theorem, confidence intervals, hypothesis testing, chi-square tests, analysis of variance, regression analysis and nonparametric statistics.
M0798 Statistical Consulting (0/3) This course covers consulting experience in data analysis and applied statistics. Students are expected to learn communication techniques and study cases from various fields of real-world data.
M1302 Special Topics in Statistical Application and Exploration (2/0) This lecture course is to provide an overview of the field for students who will continue to study cases in applied statistics.
M1408 Data Mining (2/2) This course covers techniques and case studies in Data Mining, including decision trees, neural network, association rules, and case studies.
M1651 Financial Econometrics (3/0) This course emphasizes applying econometrics to real-world problems. It is based upon developments of statistical models for estimating economic relationships and testing the theories of economics to implement policies.
M1744 Applied Statistical Methods (2/2) This long-distance learning course targets at audience who want to solve daily-life problems efficiently with software EXCEL. Statistical techniques of converting data into information are introduced through dynamical and graphical presentation. Statistical background is helpful but not necessarily required in this course.
S0061 Reliability Analysis (0/3) This course covers analysis of failure data, estimates of hazard rates and failure time distributions for the reliability of components and/or systems. Additional topics at the discretion of the instructor, if time permits.
S0075 Biostatistics (3/0) This course provides a comprehensive introduction of basic statistical approaches and focuses on biomedical applications. Students can learn how to deal with biomedical problems via statistical methods through analyzing real examples.
S0191 Regression Analysis (3/0) This course is an introduction to regression with emphasis on practical applications, including simple linear regression and multiple linear regression models, inference about model parameters and predictions, diagnostic and remedial measures about the model, independent variable selection, and multicolinearity.
S0210 Advanced Calculus (2/2) This course covers fundamental notions of limits, continuity, differentiation, and integration, for functions of one or more variables, Convergence of infinite series, and improper integrals. Prerequisite: Calculus.
S0295 Nonparametric Statistics (2/2) This course is an introduction to nonparametric statistics, including one or two sample testing and estimation methods, one or two way layout models, sign test, signed rank tests, rank tests, Mann Whitney Wilcoxon procedures, Kolmogorov Smirnov tests, and discussion and comparison with parametric methods.
S0325 Calculus (3/3) This course covers limits, differentiation and integration of functions of one variable, infinite series, functions of several variables, partial derivatives, and multiple integral.
S0408 Design of Experiments (3/0) This course is an introduction to the basic principles of experimental design. Topics include analysis of variance for experiments with a single factor, randomized blocks and Latin square designs, multiple comparison of treatment means, factorial and fractional factorial designs, and nested designs.
S0423 Mathematical Statistics (3/3) Topics of this course include sufficiency, completeness, unbiased estimation, maximum likelihood estimation, Bayes estimation, confidence intervals, tests of hypotheses, Neyman-Pearson fundamental lemma, uniformly most powerful and likelihood ratio tests. Prerequisite: Introduction to Probability Theory.
S0439 Linear Algebra (2/2) Topics of this course include matrix algebra, linear systems of equations, vector spaces, subspaces, linear dependence, rank of matrices, determinants, linear transformations, eigenvalues and eigenvectors, diagonalization, inner products and orthogonal vectors.
S0440 Linear Programming (2/2) This course is an introduction to technique for modeling and optimizing deterministic systems, computer solution of optimization problems, applications to production, logistics, and service systems.
S0450 Introduction to Probability Theory (3/3) This course is an introduction to the theory of probability, conditional probability, independence, Bayes rule, random variables and their distributions, moment generating functions. Multivariate probability distributions, covariance, distributions of functions of random variables, sampling distributions, limiting theorems and order statistics are covered. Prerequisite: Calculus.
Master's Program
B0486 Seminar on Financial Management (3/0) This course gives a comprehensive description of value-at-risk, a new benchmark to measure financial market risks. Recent VaR estimation methods' development is stressed mainly in this course.
M0118 Inventory Theory (0/3) This course is concerned with inventory models for inventory management. The main topics are basic concepts of inventory theory, deterministic inventory models, probabilistic inventory models and decision rules for inventory systems.
M0189 Sampling Theory (3/0) This course covers concepts of sampling survey, major sampling designs and its estimation procedures, and evaluation of precision of a sampling design.
M0202 Quality Control (3/0) The course is concerned with how to use modern statistical methods for quality control and improvement, including subjects from basic principles to state-of-the-art concepts and applications. The objective is to give students a sound understanding of the principles and the basis for applying them in a variety of situations.
M0303 Statistical Theory (3/3) The purpose of this course is to build theoretical statistics from the first principles of probability theory, logical development, proofs, ideas, themes, etc., evolving through statistical arguments.
M0377 Decision Making in Management (3/0) In this course, some decision theories which involve stochastic models in management science will be developed.
M0481 Categorical Data Analysis (0/3) This course is concerned with statistical methods for describing and analyzing categorical data. The main topics are the basic concepts of categorical data, chi-square test, loglinear model, and logistic model.
M0798 Statistical Consulting (0/3) This course includes topics such as consulting experience in data analysis and applied statistics. Students are expected to learn communication techniques and study cases from various fields of real-world data.
M0880 Applied Linear Model (3/0) This course provides an exposition of the theory of linear models including practical aspects of residuals and data analysis.
M0881 Special Topics in Applied Statistics (2/2) This course discusses methods and theories of applied statistics and introduces some statistical papers to students.
M0882 Case Study of Sampling Survey (0/3) The purpose of this course is to introduce the techniques of sampling survey in case study. Topics include how to construct the whole process of a sampling survey.
M0883 Statistical Computing (2/2) This course emphasizes statistical computing and simulation, including Monte Carlo simulation methods, Validation techniques, statistical analysis of simulated data, bootstrap resampling, and etc.
M0947 Data Mining (3/0) This course covers techniques and real-world applications in Data Mining, including decision trees, neural network, association rules, and case studies.
M0964 Applied Multivariate Analysis (0/3) This course is concerned with statistical methods for describing and analyzing multivariate data. Acquaintance with and use of existing statistical packages is important.
M0966 Analysis of Managerial Investment (0/3) This course teaches conceptual framework and theoretical background for investment management and decision-making by evaluating various investment strategies.
M0967 Applied Nonlinear Regression Analysis (0/3) This course provides an exposition of the theory of nonlinear regression models including practical aspects of residuals and data analysis.
M1014 Statistical Quality Control (3/0) The purpose in learning statistical quality control is to give students a strategy for effective use of statistics in the area of process control. Moreover, a paper study is conducted in this course.
M1043 Survival Data Analysis (0/3) This course provides an overview of survival data analysis, including introduction of the lifetime variable, censored data, parametric and nonparametric inference. Some advanced topics in biomedical applications will be also discussed.
M1095 Special Topics in Time Series (0/3) This course introduces some forecasting methods for time series data including ARIMA (p, d, q) transformation functions, intervention model and vector ARIMA (P, D, Q).
M1313 Econometrics (0/3) This course is an introduction to advanced skills and trainings on econometrics and the related software.
M1653 Biostatistics (3/0) This course covers the issue of basic analytical procedures, cohort study, case-control study, intervention study, categorical data analysis and survival data analysis.
M1691 Special Topics in Bayesian Inference (0/3) Bayes and empirical Bayes methods have been shown to have attractive properties for the Bayesian and the frequentist. In this course, foundation of Bayes thinking and methods is introduced. Then, several research works related to empirical Bayes ideas and methods are covered.
S0061 Reliability Analysis (0/3) This course provides some statistical methods of reliability analysis to solve practical problems including the testing of whether the reliability of a given system at a certain age is sufficiently high, and etc.
S0075 Statistical Application in Biostatistics (0/3) This course shows students how to be familiar with the use of statistics in the fields of biology and medicine. With the help of the ability of computation, statistics can provide more help for biology in order to enhance the understanding on human disease.
S0408 Design of Experiments (3/0) The course is concerned with the learning of the design and analysis of experiment in engineering applications. The statistical software package Minitab is used to conduct the data analyses of examples in the textbook so that students can follow the techniques of DOE easily.
S0594 Nonparametric Regression (3/0) Nonparametric regression is a smoothing method for recovering the unknown regression function from noisy data, without pre-specifying the functional form of the regression function. The kernel smoothing (or local polynomials) method, which is very simple and useful among other several nonparametric alternatives, will be introduced in greater detail.
T0102 Seminar (2/2) This course provides discussions in the methods and theories of statistics and studies in some reputable statistical papers.
Ph.D. Program
M1533 Advanced Statistical Inference (3/3) Topics of this course cover sufficiency and completeness, unbiased and equivariant estimators, interval estimation, Neyman-Pearson, UMP, UMPU and LR tests, asymptotic theory for estimators and tests, and other topics in modern inference.
M1626 Theory of Life Testing and Reliability (3/0) A survey of the statistical theory of reliability and life testing is introduced in this course including probabilistic failure models, complete and censored data, accelerated failure time and proportional hazards regression models with applications to accelerated life testing, system reliability, repairable system data and planning studies to obtain reliability data.
M1627 Process Capability Indices in Theory and Practice (0/3) This course covers theory and practice of process capability analysis, major process capability indices and their estimators and related distributions. Some topics of new development are also considered.
M1408 Data Mining (3/0) This course covers techniques and real-world applications in Data Mining, including decision trees, neural network, association rules, and case studies.
M1717 Special Topics in Statistical Quality Control (3/0) The course is concerned with how to use modern statistical methods to develop new techniques for quality control and improvement. The objective is to give students a training of working quality control papers.
