STAT 5501      Statistical Design Of Experiments
This course is designed to present a variety of experimental design techniques to students with moderate mathematical and statistical background. The course includes three major components: efficient factorial designs, linear and quadratic process optimization of the location parameter, and variability reduction. Students will be trained to use the SPSS statistical software package. Prerequisite: STAT 436 or consent of instructor
Credits: 3 hours
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STAT 5537      Mathematical Statistics I
Probability theory, distribution functions, sampling, statistical inference, topics in advanced applied statistics. Prerequisite: Math 402 or consent of the instructor. Note: Continued in STAT 5547.
Credits: 3 hours
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STAT 5547      Mathematical Statistics II
Continuation of STAT 5537.
Credits: 3 hours
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STAT 5551      Applied Statistical Analysis
Methods for analyzing data from experiments and observational studies; design-based and model-based inferences; model assessment; ANOVA; power analysis; SAS procedures. Prerequisites: STAT441 or consent of instructor.
Credits: 3 hours
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STAT 5561      Time Series Analysis
This course is intended to present the basis knowledge (including models, methods and concepts) of time series analysis to students with a good background in intermediate mathematical statistics. Some elementary knowledge of basic linear regression anlysis would be helpful but not necessary. The presentation will be balanced between theory and data analysis, with sufficient theory to understand the basis of methods and a broad variety of models and many real data examples. Case studies will be drawn from business and economics, network traffic and meteorology, and data will be analyzed by students using existing computer programs (SAS, Minitab and R). Students are also expected to understand proper use and limits of time series models. Prerequisites: STAT 441 or consent of instructor
Credits: 3 hours
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STAT 5565      Regression Analysis
Simple linear regression; multiple linear regression; correlation analysis; model selections; checking assumptions; regression diagonostics; combating multi-collinearity; nonlinear regression. Prerequisites: STAT 441 or consent of instructor.
Credits: 3 hours
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STAT 5572      Multivariate Analysis
Random vectors; multivariate normal distributions; Hotelling's T-square distribution; Wishart distribution; inferences on one mean vector; MANOVA; inferences on covariance matrices; profile analysis. Prerequisites: MATH 420 and STAT 441 or consent of instructor.
Credits: 3 hours
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STAT 5576      Probability
Existence and extension of measure, random variable, expectation and its properties, types of convergence, law of large numbers, weak convergence, central limit theorem, and martingale. Prerequisites: STAT 436 and MATH 5513
Credits: 3 hours
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STAT 5578      Advanced Mathematical Statistics
Exponential and location families, principles of data reduction, asymptotic distributions, advanced theory of estimation and hypothesis testing. Prerequisite: STAT 5547
Credits: 3 hours
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STAT 5588      Theory of Linear Model
This course covers vector space, full rank linear model, general inverse, estimation under linear constraints interval estimation, hypothesis testing, distributions of quadratic forms, general distribution theory, estimability, Gauss-Markov theorem, Best Linear Unbiased Estimation (BLUE), regression on dummy variables, estimation of variance components, Scheffe and Turkey intervals, and ono-full rank linear model. Prerequisites: Math 420, Stat 5537, and Stat 5565
Credits: 3 hours
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STAT 5590      Special Topics
Selected topics in various fields of mathematics. May be repeated for credit when the topic varies. Prerequisite: Consent of instructor.
Credits: 1-3 hours
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