Statistics

 

Master | Statistics

Program Format:   
Entrance Exam: GRE

The online Master of Statistics degree is the same degree that on-campus students receive, with exactly the same requirements.Thirty credit hours (10 courses) are required; all requirements are available online.

The online Master of Statistics degree is the same degree that on-campus students receive, with exactly the same requirements. Many online courses will run concurrently with on-campus courses, and lectures will be viewed as high quality recordings with features to stop or speed up the presentations and accompanied by relevant exercises. Some online courses are specially designed to take advantage of web learning and will not be concurrent with an on-campus course . In either case, online tools will be used to facilitate interaction between instructors and students and to develop a vibrant community of online learners.

ADMISSIONS REQUIREMENTS

The minimal requirements for entry are an undergraduate degree and coursework in calculus through multivariate calculus (comparable to MA 242) and linear algebra. In the application process, your credentials (transcripts and personal statement) will be evaluated for determining acceptance into the Master of Statistics online degree program. Note that GRE scores are not required, but should be uploaded if they are available.

CAREER PROSPECTS

Statistics is being used in many fields these days. The words you hear like “big data,” “data mining,” and “business analytics” are really all about statistics. See our Careers webpage for a list of more than 20 career fields that welcome expertise in statistics.

The Master of Statistics degree requires a minimum of 30 semester hours.

Required Coursework - 21 credit hours

ST 511 - Experimental Statistics For Biological Sciences I

Units: 3

Basic concepts of statistical models and use of samples; variation, statistical measures, distributions, tests of significance, analysis of variance and elementary experimental design, regression and correlation, chi-square.

Offered in Fall Spring Summer

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2017 Fall Term 2018 Summer Term 1

ST 512 - Experimental Statistics For Biological Sciences II

Units: 3

Covariance, multiple regression, curvilinear regression, concepts of experimental design, factorial experiments, confounded factorials, individual degrees of freedom and split-plot experiments. Computing laboratory addressing computational issues and use of statistical software.

Offered in Fall Spring Summer

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2017 Fall Term 2018 Spring Term 2018 Summer Term 2

ST 563 - Introduction to Statistical Learning

Units: 3

This course will introduce common statistical learning methods for supervised and unsupervised predictive learning in both the regression and classification settings. Topics covered will include linear and polynomial regression, logistic regression and discriminant analysis, cross-validation and the bootstrap, model selection and regularization methods, splines and generalized additive models, principal components, hierarchical clustering, nearest neighbor, kernel, and tree-based methods, ensemble methods, boosting, and support-vector machines.

Offered in Spring and Summer

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2018 Summer Term 2

ST 501 - Fundamentals of Statistical Inference I

Units: 3

First of a two-semester sequence in probability and statistics taught at a calculus-based level. Probability: discrete and continuous distributions, expected values, transformations of random variables, sampling distributions. Credit not given for both ST 521 and ST 501.

Offered in Fall and Summer

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2017 Fall Term 2018 Summer Term 1

ST 502 - Fundamentals of Statistical Inference II

Units: 3

Second of a two-semester sequence in probability and statistics taught at a calculus-based level. Statistical inference: methods of construction and evaluation of estimators, hypothesis tests, and interval estimators, including maximum likelihood. Credit not given for both ST 522 and ST 502.

Offered in Fall and Spring

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2018 Spring Term

ST 503 - Fundamentals of Linear Models and Regression

Units: 3

Estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markov theorem. Estimability, analysis of variance and co variance in a unified manner. Practical model-building in linear regression including residual analysis, regression diagnostics, and variable selection. Emphasis on use of the computer to apply methods with data sets. Credit not given for both ST 552 and ST 503.

Offered in Fall Only

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2017 Fall Term

ST 542 - Statistical Practice

Units: 3

This course will provide a discussion-based introduction to statistical practice geared towards students in the final semester of their Master of Statistics degree.

Offered in Spring Only

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2018 Spring Term

ST 555 - Statistical Programming I

Units: 3

An introduction to programming and data management using SAS, the industry standard for statistical practice. Detailed discussion of the program data vector and data handling techniques that are required to apply statistical methods. Topics are based on the current content of the Base SAS Certification Exam and typically include: importing, validating, and exporting of data files; manipulating, subsetting, and grouping data; merging and appending data sets; basic detail and summary reporting; and code debugging. Additional topics with practical applications are also introduced, such as graphics and advanced reporting. Statistical methods for analyzing data are not covered in this course. Regular access to a computer for homework and class exercises is required. Previous exposure to SAS is not expected.

Offered in Fall Spring Summer

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2017 Fall Term 2018 Spring Term 2018 Summer Term 1

Sample Electives - 9 credit hours

ST 556 - Statistical Programming II

Units: 3

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 and class exercises is required.

Offered in Spring and Summer

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2018 Spring Term 2018 Summer Term 2

ST 505 - Applied Nonparametric Statistics

Units: 3

Statistical methods requiring relatively mild assumptions about the form of the population distribution. Classical nonparametric hypothesis testing methods, Spearman and Kendall correlation coefficients, permutation tests, bootstrap methods, and nonparametric regressions will be covered.

Offered in Fall Only

ST 715 - Theory Of Sampling Applied To Survey Design

Units: 3

Principles for interpretation and design of sample surveys. Estimator biases, variances and comparative costs. Simple random sample, cluster sample, ratio estimation, stratification, varying probabilities of selection. Multi-stage, systematic and double sampling. Response errors.

Offered in Fall Only

ST 520 - Statistical Principles of Clinical Trials

Units: 3

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. There is also discussion of Epidemiological methods time permitting.

Offered in Fall Only

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2017 Fall Term

ST 534 - Applied Time Series

Units: 3

Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. 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 will be emphasized.

Offered in Fall Only

Find this course:

2017 Fall Term

Entry Semester Application Deadlines and Details

Dr. Donna Barton

Online Statistics Program Coordinator

College of Sciences

919.515.1916
online@stat.ncsu.edu