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113. Applied Statistics.
An introduction to statistics with emphasis on applications. Topics include the description of data with numerical summaries and graphs, the production of data through sampling and experimental design, techniques of making inferences from data such as confidence intervals, and hypothesis tests for both categorical and quantitative data. The course includes an introduction to computer analysis of data with a statistical computing package.
213. Applied Regression Analysis.
A continuation of Statistics 113 intended for students in the physical, social or behavioral sciences. Topics include simple and multiple linear regression, model diagnostics and testing, residual analysis, transformations, indicator variables, variable selection techniques, logistic regression, and analysis of variance. Most methods assume use of a statistical computing package. Prerequisite: STAT 113 or ECON 200 or permission of instructor.
226. Statistical Methods for Data Collection.
An introduction to statistical methods for data collection. Topics include methods for survey sampling and design of experiments. Survey sampling topics such as simple random sampling, survey sampling, cluster sampling, and capture/recapture sampling are covered. Design of experiment topics such as randomization, blocking, completely randomized design, randomized complete block design, Latin square, and factorial designs are covered. Statistical methods for analyzing data collected via the aforementioned methods are extensively discussed. Thorough use of a statistical software package is incorporated. Prerequisite: STAT 113 or ECON 200 or permission of instructor.
234. Introduction to Data Science.
An introduction to fundamental data science concepts using modern statistical programming languages and software. The course assumes no prior knowledge of programming but a familiarity with basic statistics is required. The course focuses on building essential data science skills such as data manipulation and visualization, basics of programming, string manipulation, and modern data sources (such as web and databases). Emphasis will be placed on building skills applicable to large scale projects. Students may receive credit for only one of STAT 234, STAT 3007, or STAT 201. Pre-reqs: STAT-113 or ECON-200.
240. Topics in Statistical Learning.
This 0.5 unit, half semester class, is intended to introduce students to a variety of tools and methods in statistical and machine learning. Topics may include unsupervised learning (i.e., cluster analysis), dimensionality reduction, and supervised learning (via classification algorithms). The emphasis will be on applying procedures to data and interpreting both numerical and graphical results. Prerequisite: STAT 201 and one of STAT 213, 226 or 342, or permission of instructor. Offered as scheduling allows.
313. Advanced Linear Models.
In previous Statistics courses, normality and independence of errors (residuals) were fundamental assumptions of the models encountered. However, in the real world, things are not always normal or independent. This course will cover more advanced techniques such as generalized linear models (including Poisson regression and Logistic regression) and multilevel models so that students can develop statistical models in a wider range of real-world situations. Pre-req: STAT-213. Offered as scheduling allows.
This course covers the theory of probability and random variables, counting methods, discrete and continuous distributions, mathematical expectation, multivariate random variables, functions of random variables and limit theorems. Prerequisite: MATH 205. Offered in the fall semester. Also offered through Mathematics.
326. Mathematical Statistics.
Following STAT 325, this course deals with the theory of parameter estimation, properties of estimators and topics of statistical inference, including confidence intervals, tests of hypotheses, simple and multiple linear regression, and analysis of variance. Prerequisite: STAT 325. Offered in the spring semester.
333. Advanced Statistical Models
In previous Statistics courses, normality, and independence of errors (residuals) was a fundamental assumption of the models encountered. However, in the real world, things are not always normal or independent. This course will cover more advanced techniques such as generalized linear models (including Poisson regression and Logistic regression) and multilevel models so that students can develop statistical models in a wider range of real-world situations. Pre-req: STAT-213 Cannot take if you already taken STAT-4003. Offered as scheduling allows.
A study of statistical techniques economists has found useful in analyzing economic data, estimating relationships among economic variables, and testing economic theories. Topics include multiple regression, PROBIT, and logit analysis, heteroscedasticity, autocorrelation, and simultaneous equations models. Prerequisites: ECON 200, 251 and 252 and MATH 135. Also offered as ECON 342.
343. Time Series Analysis.
Statistical methods for analyzing data that vary over time are investigated. Topics may include forecasting systems, regression methods, moving averages, exponential smoothing, seasonal data, analysis of residuals, prediction intervals and Box-Jenkins models. Application to real data, particularly economic data, is emphasized along with the mathematical theory underlying the various models and techniques. Prerequisite: MATH 136 or permission of the instructor. Offered every other year.
289, 389. Independent Study.
450. SYE: Senior Seminar.
489. SYE: Senior Independent Project.
498. SYE: Senior Honors Project.