• STAT 4010: Probability and Statistics for Teachers

  3.00 Credits

  Utah State University

  
  This course is an investigation of probability and statistics secondary school content from an advanced perspective and with attention to its historical development. Topics include probability, data summary techniques, parameter estimation, and inference. Prerequisite: C- or better in STAT 3000
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• STAT 4250: Advanced Internship/Co-op

  1.00 - 6.00 Credits

  Utah State University

  
  Advanced educational work experience in statistics. Prerequisite/Restriction: Approval of instructor. Repeatable for credit.
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• STAT 4500: Methods of Teaching Statistics in Secondary and Middle School

  3.00 Credits

  Utah State University

  
  Advanced educational work experience in statistics. Prerequisite/Restriction: MATH 3110 and MATH 4200 or MATH 4310 and STAT 2000 or STAT 3000 Corequisite:    MATH 4500
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• STAT 4950: Directed Reading and Conference

  1.00 - 3.00 Credits

  Utah State University

  
  This course consists of directed readings on specific topics. Prerequisite/Restriction: Prior arrangement with specific instructor. Repeatable for credit.
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• STAT 5050: Introduction to R

  1.00 Credits

  Utah State University

  
  Students install and run R, RStudio and R Markdown; write reports using R Markdown; finding R functions, objects, and documentation; issue commands and save results; understand, manipulate, and subset data types (vectors, matrices, data frames, lists); and write simple functions. Prerequisite: STAT 2000 with a C- or better (or equivalent course)
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• STAT 5080: Data Technologies

  2.00 Credits

  Utah State University

  
  Modern data technologies, including the basics of simulation, resampling, bootstrap, regular expressions, web scraping, XML, data bases and SQL, and the Tidyverse (a collection of R packages for data science) are discussed. Students use unprocessed real-life data sets whenever possible. For graduate (6000-level) credit, a major project is required. Crosslisted as: STAT 6080 Prerequisites: STAT 3000 or STAT 5100 with a C- or better STAT 5050 with a C- or better STAT 5550 is recommended
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• STAT 5100: Modern Regression Methods

  3.00 Credits

  Utah State University

  
  Students learn multiple linear regression, diagnostics for outliers and influential points, robust regression, quantile regression, collinearity, variable selection, ridge regression, the LASSO and elastic net, generalized linear models, and non-linear regression methods including generalized additive models and tree-based methods. Prerequisite/Restriction: C- or better in STAT 2000 or STAT 3000.
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  General Education Course

  This course is a general education course.

• STAT 5120: Statistical Methods for Rates and Proportions

  2.00 Credits

  Utah State University

  
  This course introduces the analysis of cross-tabulated and other categorical data, log-linear models for higher order cross-tabulations, independence graphs, logit models and logistic regression, generalized estimating equations, trend tests and other methods for ordinal classifications, and McNemar's and Cochran-Mantel-Haenszal tests. Prerequisite: C- or better in STAT 5100.
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• STAT 5150: SAS Predictive Analytics

  2.00 Credits

  Utah State University

  
  This course introduces SASr predictive analytics tools in SASr Enterprise MinerT and SASr STATT. Topics include principal components analysis; clustering observation and variables; regression with regularization and model selection; and classification including decision trees, random forests, and support vector machines. Crosslisted as: STAT 6150 Prerequisites: STAT 5650 with a C- or better
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• STAT 5170: Time Series and Forecasting

  2.00 Credits

  Utah State University

  
  Students are introduced to linear time series analysis and ARIMA models. Topics include stationarity, differencing, AR, MA, and ARIMA models, modeling and forecasting with ARIMA models. Crosslisted as: STAT 6170 Prerequisite: One of the following: STAT 5050 with a C- or better STAT 5100 with a C- or better Previous experience programming in R
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