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Course Criteria
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3.00 Credits
Develops methodology of simple random sampling, stratified sampling, and multistage samples. Provides applications related to physical, social, and biological sciences. Discusses single and two-variable estimation techniques. Presents estimation based on subsamples from subpopulations. Dual listed with STAT 4155. Prerequisite: STAT 2070 or equivalent.
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3.00 Credits
Focuses on data collection, analysis, interpretation, and communication, using contexts relevant to everyday situations. Topics chosen integrate well with the concerns of middle-level teachers and connect with such curriculum areas as health, science, and social studies. This course is not a research methods course. Cross listed with NASC 5180. Prerequisites: admission to the UW Graduate school, in either degree or non-degree seeking status, and acceptance into the Middle-level mathematics program.
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3.00 Credits
Advanced methodologies, with particular focus on concepts and methods related to regression. Topics include generalized linear models, nonlinear regression, elementary linear model theory, and Data Science topics such as resampling inference, ridge regression and the lass, and k-fold cross-validation. Prerequisites: MATH/STAT 4265/5265 and STAT 4015/5015. STAT 4025/5025 and STAT 4045/5045 are recommended.
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3.00 Credits
Advanced study of experimental designs, observational designs, and mixed models. Topics include fixed and random effects, factorial, split-plot and repeated measures designs, and hierarchical models. Linear model methodology and Data Science concepts will also be emphasized. Prerequisites: MATH/STAT 4265/5265, and at least one of STAT 4015/5015, STAT 4025/5025, or STAT 5210.
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4.00 Credits
Continuation of topics in Statistical Methods from 5220; aimed at preparing students for advanced topics courses in Statistics. Prerequisites: STAT 5220 and 5520.
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3.00 Credits
An introduction to statistical learning and data mining using techniques that have proven useful in recognizing patterns and making predictions. These techniques include both parametric and nonparametric models. Tools for computing and evaluating these techniques will also be studied. Dual listed with STAT 4240. Prerequisite: STAT 5015.
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3.00 Credits
Calculus-based. Introduces mathematical properties of random variables. Includes discrete and continuous probability distributions, independence and conditional probability distributions, independence and conditional probability, mathematical expectation, multivariate distributions and properties of normal probability law. Dual listed with STAT 4255; cross listed with MATH 5255. Prerequisite: grade of C or better in MATH 2210 or 2355.
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3.00 Credits
Presents derivations of theoretical and sampling distributions. Introduces theory of estimation and hypothesis testing. Dual listed with STAT 4265; cross listed with MATH 5265. Prerequisites: STAT 4255/5255.
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3.00 Credits
This course introduces Bayesian data analysis in an applied context. We will learn about Bayesian statistics primarily in a regression model context, taken broadly. A conceptual understanding of popular Markov Chain Monte Carlo algorithms will be provided. Dual listed with STAT 4270. Prerequisite: STAT 3050. STAT 4015/5015 recommended.
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3.00 Credits
Provides an introduction to the modeling and analysis of correlated/hierarchical data from exponential family member distributions (i.e. presence/absence, count data, Gaussian data). Emphasis is on applications. Aimed to build off of a first course in regression analysis. Dual listed with STAT 4280. Prerequisite: STAT 5015.
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