|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
Description: [Taught Fall semester in even-numbered years] Basic decision theory. Bayes' rules for estimation. Admissibility and completeness. The minimax theorem. Sufficiency. Exponential families of distributions. Complete sufficient statistics. Invariant decision problems. Location and scale parameters. Theory of nonparametric statistics. Hypothesis testing. Neyman-Pearson lemma. UMP and UMPU tests. Two-sided tests. Two-sample tests. Confidence sets. Multiple decision problems. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): MATH 567A. Identical to: MATH 567B; MATH is home department. Usually offered: Fall.
-
3.00 Credits
Description: Regression analysis including simple linear regression and multiple linear regression. Matrix formulation and analysis of variance for regression models. Residual analysis, transformations, regression diagnostics, multicollinearity, variable selection techniques, and response surfaces. Students will be expected to utilize standard statistical software packages for computational purposes. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): MATH 410 or MATH 413, or equivalent; and MATH 461 or MATH 466, or equivalent. Graduate standing. Identical to: MATH 571A; MATH is home department. Usually offered: Fall.
-
3.00 Credits
Description: Principles of designing experiments. Randomization, block designs, factorial experiments, response surface designs, repeated measures, analysis of contrasts, multiple comparisons, analysis of variance and covariance, variance components analysis. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): MATH 223 or equivalent, MATH 571A. Identical to: MATH 571B; MATH is home department. Usually offered: Spring.
-
3.00 Credits
Description: Basic theory of Bayesian inference, including analytical and numerical methods for assessing posterior and predictive distributions, and applications. Topics will include Bayesian analysis of normal linear regression and computational methods including Markov chain Monte Carlo. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): ECON 522A, ECON 522B; concurrent registration, MATH 566 and MATH 571A. Identical to: ECON 574B; ECON is home department. Usually offered: Spring.
-
3.00 Credits
Description: Analysis of contingency tables. Generalized Linear Models including logistic regression and log-linear models. Matched-pair models. Repeated categorical responses. Students will be expected to utilize standard statistical software packages for computational purposes. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): MATH 571A or equivalent. Identical to: SOC 574C. Usually offered: Spring.
-
3.00 Credits
Description: Statistical methods for environmental and ecological sciences, including nonlinear regression, generalized linear models, temporal analyses, spatial analyses/kriging, quantitative risk assessment. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): MATH 571B, or PSYC 507C, or equivalent. Identical to: CPH 574E, MATH 574E. Usually offered: Fall, Spring.
-
3.00 Credits
Description: [Taught Spring semester in odd-numbered years] Exploratory spatial data analysis, random function models for spatial data, estimation and modeling of variograms and covariances, ordinary and universal kriging estimators and equations, regularization of variograms, estimation of spatial averages, non-linear estimators, includes use of geostatistical software. Application of hydrology, soil science, ecology, geography and related fields. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): linear algebra, basic course in probability and statistics, familiarity with DOS/Windows, UNIX. Identical to: GEOG 574G; GEOG is home department. Usually offered: Spring.
-
3.00 Credits
Description: Techniques of statistical sampling in finite populations with applications in the analysis of sample survey data. Topics include simple random sampling for means and proportions, stratified sampling, cluster sampling, ratio estimates, and two-stage sampling. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): MATH 509C. Usually offered: Fall.
-
3.00 Credits
Description: Methods for analysis of time series data. Time domain techniques. ARIMA models. Estimation of process mean and autocovariance. Model fitting. Forecasting methods. Missing data. Students will be expected to utilize standard statistical software packages for computational purposes. Grading: Regular grades are awarded for this course: A B C D E. Identical to: MATH 574T; MATH is home department. Usually offered: Fall, Spring.
-
1.00 - 6.00 Credits
Description: Qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 599. Grading: Alternative grades are awarded for this course: S P F. Prerequisite(s): Student must submit Independent Study Proposal Form to GIDP Office. May be repeated: for a total of 6 units of credit. Usually offered: Fall, Spring, Summer.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|