|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
The application of multivariate statistical methods in behavioral science research. Topics include: multivariate regression, canonical correlation, discriminate analysis, factor analysis and multivariate regression, canonical correlation, discriminate analysis, factor analysis and multidimensional scaling. A wide range of computer assistance is incorporated. Dual listed with STAT 4300. Prerequisite: STAT 5050, 5060, 5070, 5080.
-
3.00 Credits
Linear models included analysis of variance, analysis of covariance, and regression within its general framework. This is a basic course in the applications of these models containing the standard topics as well as some newer and more unconventional ones. The course is oriented toward the professional statistician who will be involved in the design and analysis of experiments. Extensive use is made of SAS and BMDP in the course. Prerequisite: STAT 4025 or 5225.
-
3.00 Credits
Examines the issues surrounding the construction (item wording, test theory, and numerical scales), assessment (sampling and psychometrics), and analysis (tem analysis, qualitative data analysis, and factor analysis) of survey instruments. Roughly a third of the course is devoted to each of these areas. Dual listed with STAT 4350. Prerequisite: STAT 3050.
-
3.00 Credits
Emphasis is on a geralized linear model approach to the modeling of continuous data, placing model building and the various kriging methods into a single conceptual framework. Dual listed with STAT 4360. Prerequisite: STAT 4015.
-
3.00 Credits
Introduction to the modeling of time to event data as it arises in epidemiological and medical research. Topics include parametric and non-parametric estimation for censored data without covariates, and for data with covariates, the proportional hazards regression model, additive hazards regression model and parametric regression models. Dual listed with STAT 4370. Prerequisites: STAT 4015, 4025 and 4265.
-
4.00 Credits
Bayesian statistical methods for analyzing various kinds of data. Topics include basic Bayesian ideas and model formulation (priors, posteriors, likelihoods), single- and multiple-parameter models, hierarchical models, generalized linear models, multivariate models, survival models and an introduction to computation methods. Prerequisites: at least 2 semesters of calculus and one semester of statistics at or beyond the 4000 level.
-
3.00 Credits
An introduction to the theory of basic statistical linear models. Topics include: special matrix theory for statistics, multivariate normal distributions, distributions of quadratic forms, as well as estimation and hypothesis testing in the full rank and less than full rank models. Prerequisite: STAT 4015, 4025, 4265 and MATH 2250.
-
3.00 Credits
Designed to provide general geostatistical analyses and their applications for spatial random variables and functions. Topics covered include variogram, cross validation, kriging, cokriging, sampling strategies, and both non-conditional and conditional simulations. Several geostatistics packages will be used to analyze real field data and students are encouraged to use their own data for practicing geostatistical applications. Examples are taken from geohydrology, soil science, crop science, mining, and various environmental studies. Cross listed with SOIL/GEOL 5430. Prerequisite: STAT 4015.
-
3.00 Credits
A quantitative treatment of techniques useful in the biological sampling and estimation of animal abundance. Included are mark release methods, catch-effort methods, change in ratio methods, mortality and survival estimation, transect and quadrat sampling. Prerequisite: ZOO 4400.
-
3.00 Credits
This course is designed to develop the skill of analyzing data sets using methods of classic statistical analysis, such as analysis of variance, regression, discrete models, descriptive analysis, non-parametrics, and multivariate methods. The focus will be on understanding the various models and methods, computer assisted data analysis, and communication of results (oral and written). Prerequisite: 12 graduate level hours in statistics (excluding STAT 5000).
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|