MAT 505 Introduction to Probability (3)
Sample spaces and counting, axioms and rules of probability, conditional probability and independence, modeling with discrete and continuous random variables, jointly distributed random variables, characteristics of random variables, transformations of random variables, moment generating functions, law of large numbers and central limit theorem, statistical applications, random number generation and simulations of systems. Prerequisite: Calculus III.
MST 570 Design and Analysis of Experiments (3)
The use of experiment design early in the product cycle can substantially reduce development lead time and cost, leading to processes and products that perform better in the field and have higher reliability than those developed by using other approaches. Students will learn principles as well as implementation of experimental design in developing products and manufacturing processes that are robust to environment factors and other sources of variability.
STA 510 Regression and Analysis of Variance (3)
A continuation and further development of statistical inference within the context of linear models. Basic statistical concepts are reviewed briefly together with ordinary least squares. Multiple regression analysis is developed with a design matrix approach with an emphasis on specification and assessment of model assumptions. Analysis of Variance and Analysis of Covariance models are developed and studied. A computational environment for simulation and data analysis is integrated throughout the course. Prerequisite: MAT 370, Applied Probability or equivalent.
MST 680 Reliability and Quality Assurance (3)
This course is a study of applications of reliability-maintainability models, reliability testing and analysis, and quality engineering-design, process, control and quality transformation. Prerequisite: Statistics, Statistical Quality Control or equivalent or consent of instructor.
MAT 550 Times Series (3)
This course is an introduction to the theory and applications of times series analysis and modeling. The students will acquire a working knowledge of time series and forecasting methods as applied in economics, engineering and the natural and social sciences. Topics covered include stationary processes, ARMA and ARIMA processes, multivariate time series, state-space models, the Kalman Recursion and spectral analysis. A computational environment for simulation and data analysis is integrated throughout the course. Prerequisite: MAT 370, Applied Probability or equivalent.