The MS program in applied statistics is available to both full- and part-time students with courses available both on-campus and online. Cooperative education is optional. The program is intended for students who do not wish to pursue a degree beyond the MS. However, a number of students have attained doctorate degrees at other universities.
Plan of study
The program requires 30 credit hours and includes four core courses, electives, and a capstone or thesis.
Students are required to complete four core courses: Statistical Software (STAT-611), Regression Analysis (STAT-741), Fundamentals of Statistical Theory STAT-731, and Foundations of Experimental Design (STAT-701). Students, in conjunction with their advisers’ recommendations, should take the core courses early in the program.
Electives, capstone, or thesis
Elective courses are chosen by the student with the help of their adviser. These courses are usually department courses but may include (or transferred from other universities) up to 6 credit hours from other departments that are consistent with students’ professional objectives. The capstone course is designed to ensure that students can integrate the knowledge from their courses to solve more complex problems. This course is taken near the end of a student’s course of study. Students, with adviser approval, may write a thesis as their capstone. A thesis may be 3 or 6 credit hours. If a student writes a 6 credit hour thesis, he/she would be required to complete four elective courses instead of five.
Applied statistics, MS degree, typical course sequence
Sem. Cr. Hrs.
This course is an introduction to two statistical-software packages, SAS and R, which are often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions or macros; graphics; matrices and arrays; simulations; select statistical applications.
Fundamentals of Statistical Theory
This course introduces the students to the fundamental principles of modern graduate level statistical theory with a strong emphasis on conceptual aspects of estimation theory and statistical inference along with an exploration of the modern computational techniques needed in the application/implementation of the methods covered. Topics include fundamentals of probability theory for statistics, random variable with a focus on the understanding and use of probability distribution function (both probability density function and cumulative distribution function), quantiles of a distribution, understanding and use of the mathematical expectation operator, special discrete and continuous distributions, and distributions of functions of random variables and their use in statistical modelling, sums of random variables as used in statistics, point estimation, limit theorems, properties of estimators (bias, variance, mean squared error, consistency, efficiency, sufficiency), bias variance trade-off, interval estimation, hypothesis testing, bootstrap approach to estimation and inference, and elements of computational statistics.
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building.
Foundations of Experimental Design
This course is an introduction to experimental design with emphases on both foundational and practical aspects. Topics include the role of statistics in scientific experimentation, completely randomized designs, randomized complete block designs, Latin square designs, incomplete block designs, nested designs, general factorial designs, split-plot designs, two-level fractional factorial designs, and response-surface methodology.
Hold a baccalaureate degree (or equivalent) from an accredited university or college.
Have satisfactory background in mathematics (one year of differential equations and integral calculus) and statistics (two courses in probability and statistics).
Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
Have knowledge of a programming language.
Have a minimum cumulative GPA of 3.0 (or equivalent) (recommended but not required).
GRE scores are not required. However, in cases where there may be some question regarding the capability of the applicant to complete the program. Applicants may be asked to submit scores to strengthen their application.
Submit a current resume or curriculum vitae.
Submit two letters of recommendation from academic or professional sources.
International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 79 (internet-based) is required. A minimum IELTS score of 6.5 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.
Students must attain an overall program grade-point average of 3.0 (B) for graduation.
Maximum time limit
University policy requires that graduate programs be completed within seven years of the student's initial registration for courses in the program. Bridge courses are excluded.
Information regarding costs and the U.S. Department of Labor’s Standard Occupational Classification (SOC) code and occupational profiles for this program can be viewed on the ED Gainful Employment Disclosure Template.