Before matriculating, applicants must have completed a bachelor’s degree from an accredited college or university and have a proven track record of strong academic performance. It is not uncommon for applicants to already hold advanced degrees (MS, MBA, and PhD). We accept applicants from a wide variety of academic majors. However, students will need to have successfully completed prerequisite courses before enrolling. Prerequisites do not have to be completed before applying for admission.
Prerequisite courses for applicants include at least one, but ideally two semesters of college-level statistical methods, including substantive coursework covering regression analysis.
Although not required, an additional recommended prerequisite is experience in statistical software. There are numerous online resources for enhancing programming skill, many of them free or at low cost. Check out this free online tutorial and use of SAS software.
We recognize courses completed for credit and a grade from other accredited institutions; the course descriptions listed above should be used to guide your choice of courses from other institutions. ST 513/514 may be completed at NC State as a Non-Degree Studies (NDS) student.Further information and instructions on how to apply can be found on the NDS webpage. Registration for post-baccalaureate students opens about three weeks prior to the start of classes. Historically, the upper-level prerequisite courses fill quickly and entrance to the courses is not guaranteed.
|For students currently enrolled at NC State:||Online||Fall||Spring|
ST 305 – Statistical Methods
ST 430 – Regression Analysis
ST 371 – Probability and Distribution Theory
ST 372 – Statistical Inference and Regression
|For all other post-baccalaureate applicants:||Online||Fall||Spring|
ST 513 – Statistics for Management I
ST 514 – Statistics For Management II
Typical Subjects Covered in Prerequisite Courses
The topics provided below can be used for comparison with courses taken at other institutions.
- Statistics for Management I:
- Data Collection / Sampling
- Normal & Binomial Distributions
- Sampling Distributions / Central Limit Theorem
- Confidence Intervals
- Hypothesis Testing
- Analysis of Variance (ANOVA)
- Simple Linear Regression
- Matrix Manipulation
- Solving Systems of Linear Equations
- Gauss-Jordan Elimination
- Statistics For Management II:
- Multiple Linear Regression
- Variable Selection
- Residual Diagnostics
- Matrix Manipulation
- Least Squares Estimation / Normal Equation
- Variable Reduction through Eigenvalues
- Eigenvalues / Eigenvectors
Please send questions about prerequisites to MSA Admissions.