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## Intermediate Probability & Statistical Theory

#### Stats 200A

Basics of probability theory, random variables and basic transformations, univariate distributions (discrete and continuous, multivariate distributions).

#### Stats 200B

Random samples, transformations, limit laws, normal distribution theory, introduction to stochastic processes, data reduction, point estimation (maximum likelihood).

#### Stats 200C

Random samples, transformations, limit laws, normal distribution theory, introduction to stochastic processes, data reduction, point estimation (maximum likelihood).

## Statistical Methods

#### Stats 210 : Linear Models

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.

#### Stats 211 - Generalized Linear Models

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.

#### Stats 212 - Methods for Correlated Data

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.

## Other

#### Stats 275 - Statistical Consulting

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.

#### CS 273A - Machine Learning

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.

#### Stats 205 - Bayesian Data Analysis

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.

#### Stats 262 - Theory and Practice of Sample Survey

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.

Descriptions taken from General Catalogue 2017-18