Graduate Studies

Applied Statistics and Decision Analytics

Admission | Courses | Program | Requirements | Integrated Degree | Profile

Chairperson:  Tej Kaul
Graduate Committee Chairperson: Steven Rock
Graduate Advisor: Farideh Dehkordi-Vakil
Office: Stipes Hall 430
Telephone: (309) 298-1153 Fax: (309) 298-1020
Location of Program Offering: Macomb, Quad Cities

Graduate Faculty

  • Farideh Dehkordi-Vakil, Ph.D., University of Iowa
  • Tej K. Kaul, Ph.D., Birla Institute of Technology and Science
  • Kasing Man, Ph.D., University of Chicago
  • Alla Melkumian, Ph.D., West Virginia University
  • Steven Rock, Ph.D., Northwestern University
  • Thomas R. Sadler, Ph.D., University of Tennessee-Knoxville

Associate Professors

  • Jessica Lin, Ph.D., Binghamton University
  • William J. Polley, Ph.D., University of Iowa

Associate Graduate Faculty
Associate Professors

  • Anna Valeva, Ph.D., University of California-Santa Barbara
  • Tara Westerhold, Ph.D., University of South Carolina

Assistant Professors

  • J. Jobu Babin, Ph.D., University of Memphis
  • Shankar Ghimire, Ph.D., Western Michigan University

 Program Description

The Department of Economics and Decision Sciences offers courses leading to the Master of Science degree in Applied Statistics and Decision Analytics. Further information concerning the program and areas of specialization may be obtained from the department chairperson. The Master of Science degree is not reviewed for accreditation by AACSB International.

The Master of Science in Applied Statistics and Decision Analytics is a multidisciplinary graduate degree program with a unique focus on applied statistics and decision analytics. This program is intended for graduates from undergraduate programs in the quantitative and biological sciences, mathematics, sociology, psychology, business, computer sciences, physics, engineering, and education, as well as working professionals desiring to sharpen their data-analysis and analytical skills and learn advanced statistical methods.  The 36-semester-hour curriculum provides students with a firm foundation of statistical analysis and modeling commonly used in many fields, including education, science, technology, health care, government, business, or social science research.  The graduates of the program will be trained on industry-standard software packages, such as SAS and/or R, and gain modern analytical skills that are sought after in many fields, particularly in the areas of business and decision analytics or data analytics.  The program is designed to include 15 semester hours (s.h.) of core courses, 6 s.h. of directed electives, and 15 s.h. from one of the following: a thesis option, an internship option, or an all coursework option.

Building on the recommendations of the American Statistical Association (ASA)’s professional panel of experts (see, Amstat News, February 2013, “Preparing Master’s Statistics Students for Success: A Perspective from Recent Graduates and Employers,” graduates of our master of science in applied statistics and decision analytics degree program will be able to:

  1. apply advanced statistical methodologies, including a) descriptive statistics and graphical displays; b) probability models for uncertainty, stochastic processes, and distribution theory; c) hypothesis testing and confidence intervals; d) ANOVA and regression models (including linear, and multiple linear) and analysis of residuals from models and trends; and e) predictive modeling, forecasting, design of experiments, and stochastic models in applied statistics and decision analytics;
  2. derive and understand basic theory underlying these methodologies;
  3. formulate and model practical problems for solutions using these methodologies;
  4. produce relevant computer output using necessary and sufficient programming skills and standard statistical software (e.g., SAS, R, STATA, etc.) and interpret the results appropriately;
  5. communicate statistical concepts and analytical results clearly and appropriately to others;
  6. understand theory, concepts, and terminology at a level that supports lifelong learning of related methodologies; and
  7. identify areas where ethical issues may arise in statistics.

Career Opportunities

The need for skilled data professionals is real and growing. According to a study by the McKinsey Global Institute, United States could face a shortage of as many as 190,000 workers with “deep analytical skills” by 2018. This program seeks to combine the course work of statistical decision making and analytic tools to meet the demand for skilled workers in the U.S. and Illinois job markets. With three Fortune 100 companies in the region—John Deere, Caterpillar, and State Farm—the degree program is designed to address strong regional needs and/or a shortage of graduates in the fields of applied statistics and decision analytics. Due to the shortage of skilled data and business analysts, the market demand is strong for graduates in this field. Companies hiring include Caterpillar, John Deere, Hewlett-Packard, Honeywell, Northrop Grumman, Boeing, American Medical Association, Chicago Board of Trade, U.S. Treasury, U.S. Comptroller of the Currency, Tennessee Department of Commerce, Principal Financial Group, Bank of America, Merrill Lynch, Exxon, Illinois Power, Newsweek, and WalMart.

STEM Designation

The applied statistics and decision analytics degree program at Western Illinois University has been designated by the U.S. Immigration and Customs Enforcement agency within the Department of Homeland Security as a STEM-eligible degree program (CIP code 27.0501). The STEM designation allows eligible graduates on student visas access to an Optional Practical Training (OPT) extension, up to 36 months, as compared to 12 months for non-STEM degrees. As an international student, the longer work authorization term may help you gain additional real-world skills and experience in the U.S.

 Admission Requirements 

For admission to the Master of Science in Applied statistics and Decision Analytics degree program, students should have undergraduate preparation in a relevant area, such as, mathematics, statistics, economics, quantitative or biological sciences, sociology, psychology, business, computer sciences, physics, engineering, education. Applicants for admission to the Master of Science degree program in Applied Statistics and Decision Analytics must satisfy the standards for admission to School of Graduate Studies at Western Illinois University.

Application for admission to the School of Graduate Studies must be made online at .  Applicants must hold a bachelor’s degree from an institution that is accredited by the appropriate U.S. Department of Education regional accrediting agency.  Applicants are required to provide proof of such degree by submitting an official degree transcript for each college or university previously attended directly to the School of Graduate Studies.  Transcripts on file in the Office of the Registrar at WIU will be obtained by Graduate School personnel.

Applicants for admission to the School of Graduate Studies must have either a cumulative grade point average of at least 2.75 (based on all hours attempted at all institutions attended) for undergraduate work, OR a 3.0 or higher grade point average for the last two years (60 s.h.) of undergraduate work.

While the GRE is not required, applicants, however, are encouraged to take the GRE and submit the GRE results to strengthen their respective applications for admission in the program.

Admission to any graduate degree program at WIU is contingent upon successful completion of undergraduate coursework specified as a prerequisite. If an applicant is deficient in any or all of the minimum requirements for admission into program, such an applicant may be provisionally admitted into the program subject to the completion of all deficiencies before taking any required courses within the program. The applicants will be duly notified what deficiency courses they need to take at Western Illinois University before they will be allowed to enroll in any of the required courses in the program.

The set of deficiency courses that the applicants may be asked to complete, immediately upon being provisionally admitted into the program and depending on what the applicant may be deficient in, will be Calculus with Analytical Geometry I and II (Math 133 and Math 134) or equivalents; Linear Algebra (Math 311) or equivalent; and Introduction to Probability & Statistics/Business Statistics for Managerial Decision Making (Stat 276/DS 503) or equivalent. Students deficient in the minimum course requirements will be required to take one or more courses to remove these deficiencies prior to enrolling in the courses that are part of the program core requirements. Applicants for graduate assistantship are also required to provide at least three letters of reference from individuals who can provide meaningful comments on the student’s professional and/or academic background and a statement of interest (not to exceed two pages in length).

Students whose native language is other than English must demonstrate written and spoken English language proficiency.  Evaluation of English language proficiency will be based on the student’s scores on the Test of English as a Foreign Language (TOEFL®).  Students must meet institutionally mandated minimum TOEFL® scores as established by the WIU Center for International Studies.

Applicants are also required to provide at least three letters of reference from individuals who can provide meaningful comments on the student’s professional and/or academic background and a statement of interest (not to exceed two pages in length).

 Degree Requirements

I. Core Courses: 15 s.h.

STAT 471G Introduction to Mathematical Statistics I (3)
STAT 478G Analysis of Variance (3)
STAT 553 Applied Statistical Methods (3)
DS 435G Applied Data Mining for Business Decision Making (3)
DS 490G Statistical Software for Data Management and Decision Making (3)

II. Directed Electives: 6 s.h.

A.  Modeling and Prediction (Choose one of the following):

DS 533 Applied Business Forecasting and Planning (3)
DS 580 Predictive Analytics and Time-Series Forecasting (3)
ECON 506 Econometrics I (3)
STAT 474G Regression and Correlation Analysis (3)
PSY 551 Structural Equation Modeling for the Behavioral Sciences (3)

B.  Sampling and Experimental Design (Choose one of the following):

BIOL 501 Biometrics (3)
ECON 507 Econometrics II (3)
SOC 530 Statistical Methods (3)
PSY 501 Advanced Psychological Statistics (4)

III. Select one of the following exit options: 15 s.h.

A. Thesis

Electives* (9)
ECON 601 Thesis (3)

B. Internship Option

Electives* (6-12)
ECON 599 Internship (3-9)

C. Coursework Option

Electives* (15)

IV. Other Requirements: 0 s.h.

DS 602 Department Seminar (0), two semesters required
DS 604 Applied Statistics and Decision Analytics Assessment (0)

*Upon approval from the program graduate advisor, students may select elective courses listed above under I and II (excluding those courses that are otherwise used to fulfill the requirements under I and II) or from additional program-specific and related electives from Computer Science, Decision Sciences, Economics, Mathematics, Statistics, or other 500-level graduate courses in Research/Quantitative Methods (Techniques), Applied Business Research, etc., from Law Enforcement and Justice Administration, Management, Marketing, Sociology, Psychology, etc.


The capstone courses are fundamental in providing the knowledge and tools necessary in formulating statistical hypotheses and analyzing final results. Students must complete 36 semester hours and may follow either a Thesis or an internship or a Non‑Thesis Option. Consultation with and approval of the program graduate advisor concerning course selection is required to insure completion of all requirements. Students wishing to take a reading, or an independent study, and/or an internship course must receive approval from the program advisor prior to registration.  All special permissions or petitions must be approved prior to registration. Transfer and extension credit will be accepted in accordance with current School of Graduate Studies policy.

While all graduate students must complete the required core courses, it is possible to elect courses that will enhance specific career objectives. For further information on elective concentrations consult the program graduate advisor.

Post-Baccalaureate Certificate

The Department of Economics and Decision Sciences also offers an 18 s.h. post-baccalaureate certificate (PBC) in Business Analytics. The Business Analytics PBC offers the technical skills of data mining, statistical modeling, and forecasting for data-driven decision-making and for solving the analytical problems of the contemporary business world. For program details, go to the post-baccalaureate certificates page.

 Course Descriptions

Biology (BIOL)

501 Biometrics. (3) Basic methods of experimental design and evaluation of biological data. Prerequisite: Graduate standing in biology.

Computer Science (CS)

540 Computer Simulation. (3) Statistical techniques used in computer simulations. Construction and verification of simulation models. Programming projects. Prerequisites: One statistics course and familiarity with two programming languages.

Decision Sciences (DS)

435G Applied Data Mining for Business Decision-Making. (3)  This course provides an introduction to data mining methods for business applications. Students will learn the basics of data selection, preparation, statistical modeling, and analysis aimed at the identification of knowledge fulfilling organizational objectives. Prerequisite: STAT 171 or consent of instructor.

490G  Statistical Software for Data Management and Decision Making. (3, repeatable to 6 for different titles)  This course provides students with the basic concepts of statistical computing. Students will gain experience with statistical software packages, such as SAS or SPSS, and their applications. Methods of data preparation and validation, analysis, and reporting will be covered.

500 Introduction to Business Analytics. (1) Business analytics generally refer to the use of statistical and quantitative analysis for data-driven decision-making. This course introduces students to the foundations of business analytics problems and applications. Lectures will be supplemented with current business world examples. Prerequisite: Graduate standing .

521 Data Visualization. (2–3) This course focuses on the process and methods of visualizing information for the purpose of communicating actionable findings in a decision-making context. Hands-on experience with software for sourcing, organizing, analyzing, comprehending, reducing and visualizing data, resulting in a clear message. Prerequisites: DS 303 or equivalent, or permission of the instructor.

523 Management Science Techniques and Business Analytics. (3) Applications of management science tools and techniques for effective decision making with emphasis on model building. Topics include PERT/CPM, transportation models, linear, goal, integer and dynamic programming, and queuing theory. Prerequisite: DS 503.

533 Applied Business Forecasting and Planning. (3) A survey of the basic forecasting methods and techniques essential for modern managers. Topics include moving average and decomposition techniques, ARIMA processes, regression techniques, and technological methods such as Delphi and S-curves. Prerequisite: DS 503 or STAT 171 or equivalent.

535 Advanced Data Mining for Business. (3) This course furthers the study of data mining methods and techniques for business applications. Students will develop more advanced techniques for data preparation, information retrieval, statistical modeling and analysis aimed at the production of decision rules for specific business goals. Prerequisites: DS 435G or permission of the instructor.

540 Applied Stochastic Models in Business Analytics. (2) This course introduces stochastic models for studying phenomena in management science, operations research, finance, actuarial science, and engineering. Heuristic minded approach aimed at developing “probabilistic thinking” is taken in the treatment of probability concepts, stochastic processes, model simulation, and applications. Prerequisite: DS 303 or equivalent, or consent of instructor.

580 Predictive Analytics and Time-Series Forecasting. (3) This course introduces analytical models and tools used for continuous iterative exploration and investigation of past business performance to gain insight and drive decision. Predictive modeling, forecasting, and design of experiments will be covered. Prerequisites: DS 303 or equivalent, or permission of the instructor.

600 Independent Research. (1–3) Independent research and study of selected topics in decision sciences. Prerequisites: Completion of six graduate hours in decision sciences and permission of the Department Chairperson.

602 Department Research Seminar. (0, repeatable) A survey of contemporary theoretical and applied statistics and analytics research. Graded S/U. Prerequisite: Graduate standing.

603 Comprehensive Examination. (0) All majors are required to satisfactorily complete the knowledge assessment examination prior to graduation.  Graded S/U. Prerequisite: Economics major.

604 Applied Statistics and Decision Analytics Assessment. (0) All students in the Applied Statistics and Decision Analytics program are required to satisfactorily complete the assessment examination prior to graduation. This course also offers career preparation guidance and therefore should be taken during the student’s last semester on campus. Prerequisite: Enrollment in the Applied Statistics and Decision Analytics program.

620 Decision Sciences Internship. (1–6, not repeatable) Integrates decision sciences theories with application to actual business practices. Students are exposed to a variety of positions within the business firm during the semester. All internships are supervised by a faculty coordinator and an executive in the business firm. Analytic reports of work accomplished by each student are presented to the coordinator. Graded S/U only. Prerequisites: Completion of six hours of decision sciences courses and written permission of the Department Chairperson.

Economics (ECON)

445G Game Theory and Economic Behavior. (3)  Analysis and solution of non-cooperative games toward a deeper understanding of economic behavior. Applications include auction design, bargaining, firm market entry games, information economics, and prisoner’s dilemma type games in general. Prerequisites: ECON 232, and MATH 137 or ECON 381, or permission of instructor.

503 Applied Price Theory. (3) Application of economic theory and methods to managerial decision making. Topics include demand, cost and production analysis and estimation; forecasting; pricing policy; risk and uncertainty problems; and capital budgeting. Prerequisite: ECON 509 or equivalent.

506 Econometrics I. (3) Elements of the theory and practice of econometrics: including univariate and multivariate single equation models, statistical problems such as multicollinearity, special techniques and applications, and an introduction to simultaneous equations models. Students will complete a project involving hypothesis formulation, data collection, analysis using statistical software, and written presentation of results. Prerequisite: ECON 509 or equivalent.

507 Econometrics II. (3) Advanced econometric estimation to include estimating micro and macroeconomic functions through simultaneous equation systems, dummy dependent variable models; and multivariate analysis. Class culminates in an independent research project. Prerequisites: ECON 481G or permission of the graduate advisor, and ECON 506.

Mathematics (MATH)

552 Scientific Computing. (3) Design, analysis, and MATLAB or Mathematica implementation of algorithms for solving problems of continuous mathematics involving linear and nonlinear systems of equations, interpolation and approximation, numerical differentiation and integration, and ordinary differential equations with a significant lean toward applications. Prerequisites: MATH 311 and MATH 333, or equivalents.

Psychology (PSY)

501 Advanced Psychological Statistics. (4) A consideration of advanced statistical methods and experimental designs which are applicable to psychological research. Particular attention is given to correlation and analysis of variance. Prerequisite: PSY 223 or equivalent

551 Structural Equation Modeling for the Behavioral Sciences. (3) Structural equation modeling (SEM) and related analytical approaches employed in the behavioral sciences will be explored, with an emphasis on interpretation. Multiple regression and factor analysis will be reviewed. Hands-on training with contemporary SEM software will be provided. Prerequisites: PSY 501 or an equivalent graduate-level course that covers descriptive statistics, correlation and simple regression; or permission of the instructor.
Sociology (SOC)

432G  Survey Research. (3)  An overview of how to design, conduct, and present the results of social surveys. The course includes a familiarization with data preparation for computer processing and an introduction to using computer software statistical packages. Not open to students with credit in POLS 432. Prerequisite: any University-level Statistics course or consent of instructor.

530 Statistical Methods. (3) Modern statistical techniques and methods of data analysis in the social sciences. Data reporting, random variation and sampling procedures, interviewing, secondary data sources, the search of unobtrusive measurements, and techniques of data processing. Prerequisites: Twelve semester hours of sociology and anthropology including SOC 100 or 510, 232, 332.

531 Quantitative Methods. (3) A detailed examination of data-gathering techniques, including scaling, questionnaire construction, sampling procedures, interviewing, secondary data sources, the search for unobtrusive measurements, and techniques of data processing. Prerequisites: Completed 9–15 hours of graduate work and one undergraduate course in statistical reasoning.

532 Demographic Techniques. (3) Specialized techniques of development and analysis of population data. Original census, registration, and estimating techniques; life table construction; projections; fertility measures; use of population data; and tools of applications such as urban planning, migration analysis, and testing of sociological variables. Prerequisite: Twelve semester hours of sociology including SOC 232 and 414.

535 (cross-listed with ANTH 535) Qualitative Research Methods. (3) This course is designed to expose students to several qualitative research methods used in the social sciences. In this course, students will learn how to select the appropriate qualitative method based on the strengths, limitations and ethical dilemmas each method poses. Students will also learn how to conduct research, analyze data, and write qualitative research findings. Prerequisite: Six semester hours of sociology graduate work.

Statistics (STAT)

471G  Introduction to Mathematical Statistics I. (3)  The mathematical foundations of probability and statistics, principles of probability, sampling, distributions, moments, and hypothesis testing. Prerequisite: MATH 138 or 231 or equivalent.

472G  Introduction to Mathematical Statistics II. (3)  Continuation of STAT 471 including further topics in estimation and hypothesis testing. Prerequisite: STAT 471.

474G  Regression and Correlation Analysis. (3)  Least squares theory; correlation theory; simple, multiple, and stepwise regression; computer assisted model building; and applied problems. Prerequisite: STAT 276 or equivalent.

478G  Analysis of Variance. (3)  A study of analysis of variance and covariance with applications. Includes experimental design. Prerequisite: STAT 276 or equivalent.

553 Applied Statistical Methods. (3) Introduction to probability and statistics with a significant lean toward applications. Topics include probability, probability distributions, Central Limit Theorem, sampling distributions (t, F, Chi-Square), parameter estimation, hypothesis testing, nonparametric statistics, ANOVA, and linear regression. Prerequisites: MATH 231 and STAT 276, or equivalents.

570 Probability Theory and Stochastic Processes. (3) Nature of probability theory, sample space, combinatorial analysis, fluctuations in random events, stochastic independence, random variables, generating functions, Markov chains, and simple time-dependent stochastic processes. Prerequisite: STAT 471 or equivalent.

574 Linear Models and Experimental Designs. (3) General linear models, Gauss Markov Theorem, experimental design model confounding, and types of experimental designs and their analysis. Prerequisite: STAT 472 or permission of the instructor.

653 Elements of Statistical Inference. (3) A study of elements of statistical inference with a lean toward developing the theory. Topics include probability theory, random variables, probability distribution functions, limit theorems, estimation, testing, sufficiency, robust statistical methods, bootstrap, and linear models. Prerequisites: STAT 471 and STAT 553.