Applicants must hold an undergraduate degree from an accredited institution of higher learning. Applicants should have academic backgrounds of excellence, usually with majors, or equivalent, in the fields in which they intend to study for advanced degrees. Normally, a B average (or equivalent) from an accredited college is required. With evidence of special promise, such as high Graduate Record Examination scores, an applicant whose academic record falls short of a B average may be accepted on a conditional basis. Meeting the minimum requirements does not assure acceptance. The departments may, and often do, set higher admission standards. Moreover, the number of spaces available for new graduate students limits the number who can be accepted. Students who apply in their senior year must provide evidence of the completion of their baccalaureate work before registration in Columbian College is permitted. Applicants should be aware that graduate courses taken prior to admission while in non-degree status are not used in assessing admissibility to degree programs and may not be transferable into those programs.

If desired, a student may complete the MS program prior to admission to the Ph.D. degree program, in which case no more than 24 credit hours from the MS degree may be applied to the PhD course work requirements. In this instance the student will be required to take a minimum of 27 additional credit hours of coursework. The distribution of these courses between statistics and public health would depend on the nature of the master's degree and whether the transferred credit hours would be used to defray statistics or public health course work. Full information is available in the online Graduate Admissions Application.

A detailed description of admissions policies is also available online.

Minimum Prerequisite Courses for Admission Consideration

The courses listed below (or equivalents) are prerequisites for admission consideration, and MUST appear on your transcript. Submit your MS Biostatistics program admission application only after you have completed all of the following courses:

Course Number Course Title Credits Description
MATH 1231 Single-Variable Calculus I 3 Limits and continuity. Differentiation and integration of algebraic and trigonometric functions with applications.
MATH 1232 Single-Variable Calculus II 3 The calculus of exponential and logarithmic functions. Techniques of integration. Infinite series and Taylor series. Polar coordinates. Prerequisite: MATH 1231
STAT 2118 Regression Analysis 3 Lecture 3 credits, laboratory 1 hour. Simple and multiple linear regression, partial correlation, residual analysis, stepwise model building, multicollinearity, and diagnostic methods, indicator variables. Prerequisite: Introductory Statistics


Additional Course Requirements

The courses listed below are “Additional Course Requirements.” Applicants lacking these courses (or equivalents to these GW courses) will be considered for admission, but, if admissible, will be admitted conditionally with the expectation that these courses will be satisfactorily completed within two semesters following matriculation in the program. These credits do not count as credit toward the 33 credit graduation requirement, nor are grades earned in these additional courses reflected in the overall grade point average.

Course Number Course Title Credits Description
MATH 2233 Multivariable Calculus III 3 Partial derivatives and multiple integrals. Vector-valued functions. Topics in vector calculus, including line and surface integrals and the theorems of Gauss, Green, and Stokes. Prerequisite: MATH 1232
MATH 1284 Linear Algebra I 3 Linear equations, matrices, inverses, and determinants. Vector spaces, rank, eigenvalues, and diagonalization. Applications to geometry and ordinary differential equations. Prerequisite: MATH 1231
STAT 1129 Introduction to Computing 3 Introduction to personal and mainframe computers and their operating system, spreadsheets with simple statistical applications, and programming with applications to technology. Fall and Spring.
STAT 2183 Intermediate Statistical Laboratory: Statistical Computing Packages 3 Application of program packages (e.g., SAS, SPSS) to the solution of one-, two- and k-sample parametric and nonparametic statistical problems. Basic concepts in data preparation, modification, analysis and interpretation of results. Prerequisite: an introductory statistics course. Fall and Spring.
PubH 6249 Use of Statistical Packages: Data Management and Data Analysis 3 This course familiarizes the student with one of the most widely used database management systems and statistical analysis software packages, the SAS System, operating in a Windows environment. Throughout the course, several database management system techniques and data analytical strategies for the appropriate analysis of datasets obtained from a variety of studies will be presented. Statistical techniques covered include linear regression, analysis of variance, logistic regression, and survival analysis. Fall and Spring.