|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
Introduction to stochastic modeling theory and application. Topics include probability theory, Markov processes, renewal theory, and queuing theory. Additional topics include stochastic dynamic programming and stochastic programming. Prerequisite: Eng Mgt 365. (Co-listed with Eng Mgt 438)
-
3.00 Credits
An introduction to cluster analysis and clustering algorithms rooted in computational intelligence, computer science and statistics. Clustering in sequential data, massive data and high dimensional data. Students will be evaluated by individual or group research projects and research presentations. Prerequisite: At least one graduate course in statistics, data mining, algorithms, computational intelligence, or neural networks, consistent with student's degree program. (Co-listed with Comp Eng 439, Elec Eng 439, Sys Eng 439 and Comp Sci 449)
-
3.00 Credits
Development and application of Poisson and nonhomogeneous Poisson processes; renewal processes; Markov chains and processes including birth and death processes; and normal processes, including Brownian motion. Prerequisites: Stat 343 and Math 204 or 229.
-
3.00 Credits
A course covering distribution free statistical methods. Topics include: order statistics, tests of hypotheses for one-sample and two-sample problems, analyses of variance, goodness-of-fit tests, runs test, independence and regression problems, point and interval estimation, ARE. Prerequisite: Stat 344.
-
3.00 Credits
Experimental designs and their statistical analysis. Includes completely randomized designs, complete and incomplete blocking designs, factorial and fractional factorial experiments, multiple comparisons, response surface analysis. Prerequisites: One of Stat 353, Eng Mg 387 and one of Stat 211, 213, 215, 217, 343; or Stat 343 and one of Stat 211, 213, 215, 217.
-
3.00 Credits
Analysis of data consisting of simultaneous measurements on many variables. Multivariate normal distribution, multivariate analysis of variance, canonical correlation, principal components, classification and clustering techniques. Prerequisites: Stat 344 and Math 203.
-
3.00 Credits
Probability spaces, random variables, distribution functions, expectations, independence, convergence theorems, characteristic functions, moment generating functions, and central limit theorem. Prerequisites: Stat 344 and Math 315.
-
3.00 Credits
Includes a development of the theory of the distribution of quadratic forms, and the estimation of parameters and testing hypotheses in linear statistical models. Prerequisites: Math 208 and Stat 343 and either Stat 353 or 344.
-
3.00 Credits
Includes the theory of polynomial models, regression models, experimental design models, incomplete block models, nonlinear models, with emphasis on optimum properties of point and interval estimation and the power of tests. Prerequisite: Stat 453.
-
3.00 Credits
The theory of estimation and hypothesis testing. Completeness and sufficience. Maximum likelihood, minimax, Bayesian and invariant procedures for testing. Sequential desision rules. Emphasis on estimation. Prerequisites: Stat 344 and Math 315.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|