Statistics Online Graduate Courses

  • Prerequisites: ST 501
  • Offered each Spring
  • 3 credit hours
  • This course is designed to provide the basic tools of statistical inference to graduate students. It should prepare the students to understand the foundations behind statistical inference, and enable them to formulate appropriate statistical procedures. It should further hone their problem solving skills, as well as prepare them to handle more advanced courses.
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  • Instructor: Justin Post
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  • Prerequisites: ST 501, ST 502 is a co-requisite
  • Offered each Fall
  • 3 credit hours
  • This course covers the theory underlying linear statistical models as well as practical experience with model-building methods such as residual analysis and variable selection.
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  • Instructor: Howard Bondell
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  • Prerequisites: Graduate Standing
  • Offered in Fall and Spring
  • 3 credit hours
  • Basic introduction to methods of statistics for students who are graduate students in a variety of social sciences.
  • A general introduction to the use of descriptive and inferential statistics in behavioral science research. Methods for describing and summarizing data presented, followed by procedures for estimating population parameters and testing hypotheses concerning summarized data.
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  • Prerequisites: ST 507
  • Offered each Spring
  • 3 credit hours
  • Statistics for the Behavioral Sciences II is a non-calculus-based, second course in statistics that will assist students with the analysis of data generated from research in the social sciences. Students will learn several methods for determining the relationships between variables. Emphasis is placed on learning when to use a particular method and interpreting models and related computer output. The use of mathematical formulas and notation is minimized. A literature review, which involves creating a summary paper and presentation, allows the student to relate the course material to his/her specific discipline.
  • Simple linear regression, multiple regression, logistic regression, analysis of variance, analysis of covariance and model selection. Statistical software, such as SAS and Statcrunch, will be used.
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  • Prerequisites: ST 311 or graduate standing
  • Offered Fall and Summer I
  • 3 credit hours
  • This first course in statistics for graduate students is intended to give students a background in the statistical methods that will assist them in the analysis of data generated from research in the biological sciences. Students will learn several methods for summarizing and describing data in addition to techniques for using sample data to make inferences about a larger population. This is a non-calculus-based course. Students will be introduced to statistical software; however, there is not a lab associated with this course.
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  • Prerequisites: ST511
  • Offered in Fall, Spring, and Summer II
  • 3 credit hours
  • ST512 is an applied course that introduces statistical methods based on linear models for continuous response variables commonly used in designed experiments. Examples include multiple linear regression, factorial designs, and split-plot experiments. It is a prerequisite for most advanced courses in statistics.
  • Simple and multiple regression. One- and two-factor ANOVA. Blocked and split-plot designs. A new section of ST 512 beginning Fall 2014 will add a focus on categorical data analysis including regression with binary response Y (logistic regression) and analysis of data with multiple sources of error such as longitudinal data collected over time.
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  • Instructor: Roger Woodard
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  • Prerequisites: Graduate Standing
  • Offered in Fall and Spring
  • 3 credit hours
  • Basic introduction to methods of statistical inference and modeling. SAS is introduced. Analysis of data to represent facts, guide decisions and test opinions in managing systems and processes. Graphical and numerical data analysis for descriptive and predictive decisions. Scatter plot smoothing and regression analysis. Basic statistical inference. Integrated use of computer.
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  • Instructor: Roger Woodard
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  • Prerequisites: ST513 or ST511 or ST507
  • Offered in Spring and Summer I
  • 3 credit hours
  • Regression analysis is a flexible statistical problem solving methodology. Students will learn the about regression analysis in depth from topics on basic regression through more advanced techniques. Students will gain considerable experience working with data. Data from examples and problems in the text. Students will use SAS to do most homework assignments.
  • Simple linear regression, Regression analysis using linear algebra, multiple linear regression, model building techniques and strategies, variable selection techniques, common pitfalls of regression, residual analysis, logistic regression.
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  • Instructor: Roger Woodard
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  • Prerequisites: Second course in calculus (MA 241 at NCSU)
  • Offered Summer I
  • 3 credit hours
  • This first course in statistics for graduate students is intended to give students a background in the statistical methods that will assist them in the analysis of data. Students will learn methods for summarizing and describing data in addition to techniques for using sample data to make inferences about a larger population. This is a non-calculus-based course. Students will be introduced to statistical software; however, there is not a lab associated with this course.
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  • Instructor: Herle McGowan
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  • Prerequisites: ST507 or ST511 or equivalent statistical knowledge and Graduate Standing
  • Offered each Spring
  • 3 credit hours
  • This course is designed to bridge theory and practice of how students develop understandings of key concepts in data analysis, statistics, and probability. Discussion of students’ understandings, teaching strategies and the use of manipulatives and technology tools (graphing calculators, Fathom, TinkerPlots, Excel). Course will help prepare students for designing and teaching courses in statistics.
  • Topics include distribution, measures of center and spread, sampling, sampling distribution, bivariate analysis, correlation, randomness, and law of large numbers. Many readings are assigned from book chapters and research journals to familiarize students with theories and frameworks in statistics education research and teaching.
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  • Co-rerequisites: ST 501 or ST 521
  • Offered each Fall
  • 3 credit hours
  • Background and Goals: This course will introduce some basic statistical concepts and methods used in Epidemiology and will focus on the statistical principles and methods used in clinical trials, including phase I to IV clinical trials.
  • Content: Statistical methods for design and analysis of clinical trials and epidemiological studies. Phase I, II, and III clinical trials. Principle of Intention-to Treat, effects of non-compliance, drop-outs. Interim monitoring of clinical trials and data safety monitoring boards. Introduction to meta-analysis. Epidemiological design and methods
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  • Instructor: Anastasios (Butch) Tsiatis
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  • Prerequisites: ST 512 or ST 514 or ST 515 or ST 517
  • Offered each Fall
  • 3 credit hours
  • Background and Goals: The goal of this course is to introduce the most important methods for analyzing time series data, from both the time domain and frequency domain perspectives.
  • Content: Statistical modelling and analysis techniques of time series data using R will be presented. Exploratory data analytic tools and inference on important time series features, such as trend, seasonality, long memory, non-stationarity, etc., using both time domain and frequency domain methods, will be covered. A brief review of necessary statistical concepts and R will be given at the beginning. Analyses of real data sets using the statistical software packages in R will be emphasized.
  • Text: Time Series Analysis and Its Applications, 4th edition, by Robert H. Shumway and David S. Stoffer
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  • Instructor: Soumendra Lahiri
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  • Prerequisites: ST 512 or 514 or 515 or 517
  • Offered each Spring
  • 3 credit hours
  • Background and Goals: The goal of this course is to introduce Bayesian data analysis methods to students who do not have a theoretical background in statistics.
  • Content: Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS); linear and hierarchical models; model selection and diagnostics.
  • Text: Doing Bayesian Data Analysis, 2nd Edition. J. Kruschke
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  • Instructor: Bryan Reich
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  • Prerequisites: (ST 508 or 512 or 514 or 518) and (ST 502 or 522)
  • Offered each Spring
  • 3 credit hours
  • Background and Goals: This course provides a discussion-based introduction to statistical practice geared towards students in the final semester of their Master of Statistics degree.
  • Content: Students will practice writing and presenting throughout the semester, gaining soft skills that are not necessarily covered in their other courses. Students will work alone on a carefully selected consulting project.
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  • Instructor: Emily Griffith
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  • Prerequisites: Graduate Standing
  • Offered Fall, Spring, Summer
  • 3 credit hours
  • Computing skills have become increasingly important in our graduate curriculum in Statistics. There is an increased need to train students to handle the scale and scope of the data facing modern statisticians. Students must know how to read data from a variety of sources and in a variety of formats, validate the data for errors, manipulate the data in meaningful ways, subset and group data, merge/append data sets, and process data in and automated fashion to produce summary reports and to export the data for statistical analysis.
  • An introduction to the data handling techniques that are required to apply statistical methods including the importing, validating and exporting of data files; manipulating, subsetting and grouping data; merging and appending data sets; and basic reports including tables and graphics. Students learn SAS, the industry standard for statistical practice. Regular access to computer for homework and class exercises is required.
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  • Instructor: Jonathan Duggins
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  • Prerequisites: Graduate Standing
  • Offered in Spring and Summer II
  • 3 credit hours
  • Computing skills have become increasingly important in our graduate curriculum in Statistics. There is an increased need to train students to handle the scale and scope of the data facing modern statisticians. Students must know how to read data from a variety of sources and in a variety of formats, validate the data for errors, manipulate the data in meaningful ways, subset and group data, merge/append data sets, and process data in and automated fashion to produce summary reports and to export the data for statistical analysis.
  • Statistical procedures for importing/managing complex data structures using SQL, automated analysis using macro programming, basic simulation methods and text parsing/analysis procedures. Students learn SAS, the industry standard for statistical practice. Regular access to a computer for homework
  • Recent Syllabus
  • Instructor: Jason Osborne
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  • Prerequisites: ST507 or ST511 or equivalent statistical knowledge and Graduate Standing
  • Offered Online only, check availability
  • 3 credit hours
  • This course will provide statistics educators with an in-depth introduction to technology for teaching statistics. In this course students will be explore a variety of available statistical packages, demonstration applets, and other technologies for teaching statistics. Students will learn pedagogy to help them structure learning activities around these technologies. Students will also learn to identify key elements in technologies that support pedagogical goals.
  • Topics include GAISE Guidelines, teaching simulations and modeling, data collection technologies and student generated data, online assessment systems and interactive data analysis, hybrid and online teaching.
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  • Prerequisites: ST 512 or 514 or 515 or 517
  • Check availability (first offered online Spring 2018)
  • 3 credit hours
  • This is a hands-on course using modeling techniques designed mostly for large observational studies. Estimation topics include recursive splitting, ordinary and logistic regression, neural networks, and discriminant analysis. Clustering and association analysis are covered under the topic “unsupervised learning,” and the use of training and validation data sets is emphasized. Model evaluation alternatives to statistical significance include lift charts and receiver operating characteristic curves. SAS Enterprise Miner is used in the demonstrations, and some knowledge of basic SAS programming is helpful.
  • Recent Syllabus
  • Text: Applied Analytics Using SAS Enterprise Miner, free pdf file available for registered course participants
  • Instructor: David A. Dickey
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  • Prerequisites: ST 512 or 514 or 515 or 517
  • Offered Summer II
  • 3 credit hours
  • The focus of this course is on regression and classification methods for applied supervised learning. Topics covered will include linear and polynomial regression, logistic regression and linear discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, nearest neighbor and tree-based methods, random forests and boosting, and support-vector machines. Unsupervised learning methods such as principal components and hierarchical clustering will also be discussed. The R software language will be used in the course, but no prior experience with R is required.
  • Recent Syllabus
  • Text: An Introduction to Statistical Learning with Applications in R, by James, Witten, Hastie, and Tibshirani (2013).
  • Instructor: Howard Bondell
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  • Prerequisites: ST 512 or 514 or 515 or 517
  • Check availability (scheduled to start in Fall 2016)
  • 3 credit hours
  • Background and Goals: This course explores the world of data on the internet and in large observational data bases. Statistical and computational methods for handling these data are studied.
  • Content: This course covers the core statistical concepts needed to analyze large, observational datasets. Topics include sampling design, basics of estimation and inference, scaling statistical algorithms to large data sets or streaming data, and dealing with complex data structures.
  • Instructor: Eric Laber
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Upcoming Application Deadlines

  • Fall Deadline: June 25, 2017
  • Spring Deadline: Nov. 15, 2017

Upcoming Courses

Important Dates

Summer II 2017: June 26–August 1
Fall 2017: August 16–December 13

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Tuition and fee information for the upcoming semester can be found on the Cashier's Office & Student Accounts website.

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