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Basics of probability theory, random variables and basic transformations, univariate distributions (discrete and
continuous, multivariate distributions).
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Random samples, transformations, limit laws, normal distribution theory, introduction to stochastic processes, data
reduction, point estimation (maximum likelihood).
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Statistical methods for analyzing data from surveys and experiments. Topics include randomization and model-based
inference, two-sample methods, analysis of variance, linear regression and model diagnostics.
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Development of the theory and application of generalized linear models. Topics include likelihood estimation and
asymptotic distributional theory for exponential families, quasi-likelihood and mixed model development. Emphasizes
methodological development and application to real scientific problems.
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Development and application of statistical methods for analyzing corrected data. Topics covered include repeated
measures ANOVA, linear mixed models, non-linear mixed effects models, and generalized estimating equations. Emphasizes
both theoretical development and application of the presented methodology.
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Training in collaborative research and practical application of statistics. Emphasis on effective communication as it
relates to identifying scientific objectives, formulating a statistical analysis plan, choice of statistical methods,
and interpretation of results and their limitations to non-statisticians.
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Computational approaches to learning algorithms for classifications, regression, and clustering. Emphasis is on
discriminative classification methods such as decision trees, rules, nearest neighbor, linear models, and naive Bayes.
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Basic Bayesian concepts and methods with emphasis on data analysis. Special emphasis on specification of prior
distributions. Development for one-two samples and on to binary, Poisson and linear regression. Analyses performed
using free OpenBugs software.
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Introduction to the basics of sampling from both applied and theoretical perspectives. Methods
covered include simple random sampling, stratified sampling, cluster sampling, sampling with unequal probabilities, and multistage
sampling. Ratio estimate, regression estimate, and methods to handle nonresponse will also be presented.
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* Descriptions taken from