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Course Criteria
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3.00 Credits
II; 3 cr (N-A). Theory and applications of multivariate statistical methods. Basic concepts and statistical reasoning which underlie the techniques of multivariate analysis. Ideas rather than derivations stressed although basic models discussed to give the student some feeling for their adequacy in particular situations. Current applications in the functional areas of accounting, finance, marketing and management. Acquaintance with and use of existing computer programs in the multivariate analysis area. P: Gen Bus 304, Stat 314 or equiv.
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3.00 Credits
I, II; 3 cr (N-A). Problems of enumeration, distribution, and arrangement. Inclusion-exclusion principle. Generating functions and linear recurrence relations. Combinatorial identities. Graph coloring problems. Finite designs. Systems of distinct representatives and matching problems in graphs. Potential applications in the social, biological, and physical sciences. Puzzles. Problem solving. P: Math 320 or 340 or cons inst.
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3.00 Credits
I, II; 3 cr (N-A). Real linear algebra over polyhedral cones; theorems of the alternative for matrices. Formulation of linear programs. Duality theory and solvability. The simplex method and related methods for efficient computer solution. Perturbation and sensitivity analysis. Applications and extensions, such as game theory, linear economic models, and quadratic programming. P: Math 443 or 320 or 340 or cons inst.
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3.00 Credits
I; 3 cr (r-N-I). Course designed for the biomedical researcher. Topics include: descriptive statistics, hypothesis testing, estimation, confidence intervals, t-tests, chi-squared tests, analysis of variance, linear regression, correlation, nonparametric tests, survival analysis and odds ratio. Biomedical applications used for each topic. P: Math 221 or equiv or cons inst.
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3.00 Credits
II; 3 cr (I). Intended for biomedical researchers interested in the design and analysis of clinical trials. Topics include definition of hypotheses, measures of effectiveness, sample size, randomization, data collection and monitoring, and issues in statistical analysis. Statistics graduate students should take Stat 641. P: Stat 541 or equiv or cons inst.
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3.00 Credits
II; 3 cr (I). Provides practice in analysis and interpretation of existing datasets from national and international clinical trials in a variety of diseases. Students will develop a research question, review clinical protocols, and analyze available data to prepare a report. P: Stat 541 or 572 & Stat 542 or 641.
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3.00 Credits
I; 4 cr (r-I). Descriptive statistics, distributions, one- and two-sample normal inference, power, one-way Anova, simple linear regression, categorical data, non-parametric methods; underlying assumptions and diagnostic work. P: College algebra: Grad st or cons inst.
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3.00 Credits
II; 4 cr (I). Continuation of Forestry 571. Polynomial regression, multiple regression, two-way Anova with and without interaction, split-plot design, subsampling, analysis of covariance, elementary sampling, introduction to bioassay. P: Stats/Forestry/Hort 571.
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3.00 Credits
I; 3 cr (A). Detecting and quantifying spatial patterns and modeling in the presence of such patterns. Spatial Point Patterns: testing nonrandomness, simulating and characterizing patterns. Lattice Data: spatial autocorrelation and regression. Geostatistics: variograms, ordinary and universal kriging, inference, assessing assumptions, and extensions. P: Stat 333 & 424; or Stat/Forest/Hort 572; or cons inst.
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3.00 Credits
I; 3 cr (A). Review of probability, random variables and vectors and their distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts for sequences of random variables, laws of large numbers, central limit and other limit theorems. P: Stat 309 or 431, Math 340, Math 521, or equiv or cons inst.
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