ms emphasis in industrial statistics and six sigma methodology

Note: The MS in Statistics program has the following prerequisite courses:

MAT 270, 271, 272 - Calculus
MAT 371 - Advanced Calculus
MAT 342 - Linear Algebra
CSE 100 - Computer Programming
QBA 221 or STP 420 - Introductory Applied Statistics

The following courses are all required for the MS Statistics degree with an emphasis in industrial statistics and Six Sigma methodology:
(For general information about the Master of Science in Statistics program click here)

STP 421 - Probability
STP 427 - Mathematical Statistics
STP 526 - Theory of Linear Models
STP 530 or IEE 578 or ECN 525 - Regression Analysis
IEE 572 - Design of Experiments
IEE 570 - Advanced Quality Control
IEE 581 - Six Sigma Methodology
IEE 585 - Six Sigma Capstone

In addition, two electives will be chosen from the following list:

IEE 520 - Statistical Learning for Data Mining
IEE 545 - Simulating Stochastic Systems
IEE 571 - Quality Management
IEE 573 - Reliability Engineering
IEE 579 - Times Series Analysis and Forecasting
IEE 582 - Response Surfaces and Process Optimization
IEE 672 - Advanced Topics in Experimental Design (requires IEE 582)
IEE 677 - Regression and Generalized Linear Models
IEE 679 - Time Series Analysis and Control (requires IEE 579)
ECN 527 - Categorical Data Analysis
ECN 535 - Multivariate Methods
ECN 540 - Forecasting
STP 531 - Applied Analysis of Variance (or ECN 530)
STP 532 - Applied Nonparametric Statistics
STP 533 - Applied Multivariate Statistics
STP 534 - Applied Discrete Data Analysis
STP 535 - Applied Sampling Methodology
STP 598 - Special Topics

The program director will write a letter that outlines the training and project and send it to any employer or potential employer who may be considering the recipient for black belt certification. Course work for the Master of Science in Statistics with emphasis in Industrial Statistics may be applied to the Six Sigma Black Belt Certification program offered by the Center for Professional Development.