Courses

This 30-credit master's program can be completed in two to five years, depending on whether you take one or two courses each semester. The goal is to provide graduates with broad knowledge in a wide range of statistical application areas — and the employable skills in statistics that are now in high demand. Of the 30 credits required to graduate, 24 must be courses from the statistics department, and 21 must be at the 500 level. A minimum grade-point average of 3.0 is also required for graduation.

Most courses within the Applied Statistics program are also available as individual courses for those looking to fulfill continuing professional development requirements. Read the instructions for how to register for Penn State World Campus courses to learn how you can enroll in any of the upcoming classes on an individual basis.

Build Your Professional Network

Your fellow students will have bachelor's degrees in agricultural, biological, business, computer, engineering, mathematical, physical or social sciences, and other related fields. The online courses are highly interactive and collaborative, allowing you to build strong ties with others and gain perspectives from other disciplines and industries.

Course List - Master of Applied Statistics

Required Courses (15 credits)
Title	Abbreviation	Description	Credits
Introduction to Probability Theory	STAT 414	Probability spaces, discrete and continuous random variables, transformations, expectations, generating functions, conditional distributions, law of large numbers, central limit theorems. Prerequisites: Statistics — STAT 500 and STAT 501 strongly recommended. Math — A standard three-course calculus sequence (for example, MATH 140, MATH 141 and MATH 230) and knowledge of matrix algebra/linear algebra (MATH 220). If taken more than a few years ago, students are strongly encouraged to review their calculus knowledge.	3 credits
Introduction to Mathematical Statistics	STAT 415	A theoretical treatment of statistical inference, including sufficiency, estimation, testing, regression, analysis of variance, and chi-square tests. Prerequisites: STAT 414	3 credits
Regression Methods	STAT 501	Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression. Prerequisites: 6 credits of statistics or STAT 500 plus matrix algebra	3 credits
Analysis of Variance and Design of Experiments	STAT 502	Analysis of variance and design concepts; factorial, nested, and unbalanced data; ANCOVA; blocked, Latin square, split-plot, repeated measures designs. Prerequisites: STAT 501	3 credits
Statistical Consulting Practicum I	STAT 580	General principles of statistical consulting and statistical consulting experience. Preparation of reports, presentations, and communication aspects of consulting are discussed. Prerequisites: STAT 502, STAT 503 or STAT 504 or STAT 506	2 credits
Statistical Consulting Practicum II	STAT 581	Statistical consulting experience including client meetings, development of recommendation reports, and discussion of consulting solutions. Prerequisites: STAT 580	1 credit

Elective Courses (15 credits)
Title	Abbreviation	Description	Credits
Applied Nonparametric Statistics	STAT 464	Tests based on nominal and ordinal data for both related and independent samples. Chi-square tests, correlation. Prerequisites: STAT 200, STAT 220, STAT 240, STAT 250, STAT 301 or STAT 401	3 credits
Introduction to SAS	STAT 480	Introduction to SAS with emphasis on reading, manipulating and summarizing data. Prerequisites: 3 credits of statistics or STAT 500	1 credit
Intermediate SAS for Data Management	STAT 481	Intermediate SAS for data management. Prerequisite: STAT 480	1 credit
Advanced Statistical Procedures in SAS	STAT 482	This course covers advanced statistical procedures in SAS, including ANOVA, GIM, CORR, REG, MANOVA, FACTOR, DISCRIM, LOGISTIC, MIXED, GRAPH, EXPORT, and SQL. Prerequisites: STAT 480, STAT 481	1 credit
Statistical Analysis System Programming	STAT 483	Introduction, intermediate, and advanced topics in SAS. Note: Credit cannot be received for both STAT 483 and STAT 480/481/482. Prerequisites: 3 credits in statistics	3 credits
Topics in Stat Computing with R	STAT 484	Topics include: I) Basic background on R II) Manipulating data I III) Finding help IV) Simple univariate data V) Importing data VI) Documenting your work VII) Manipulating data II VIII) Repetitive tasks — loops and the apply () family IX) Visual data X) Basic analyses (as much as possible you'll be working with real data).	1 credit
Intermediate R Statistical Programming Language	STAT 485	Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics. Enforced Concurrent at Enrollment: STAT 484	1 credit
Introduction to Statistical Analysis with Python	STAT 487	Due to the pervasiveness of Python as a statistical analysis tool, there is a demand for statisticians to learn Python to perform descriptive and inferential data analysis. The course will take a case study approach to introduce students to Python. Students will learn to work with complex data using Python and will get hands-on experience on how to use Python to conduct statistical analyses. Enforced Prerequisite at Enrollment: STAT 300 or STAT 460 or STAT 461 or STAT 462 or STAT 500	2 credits
Applied Statistics	STAT 500	Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and 2-way ANOVA, Chi-square tests, diagnostics. Prerequisites: 3 credits of undergraduate course work in statistics	3 credits
Design of Experiments	STAT 503	Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs. Prerequisites: STAT 501, STAT 502	3 credits
Analysis of Discrete Data	STAT 504	Models for frequency arrays; goodness-of-fit tests; two-, three-, and higher- way tables; latent and logistic models. Prerequisites: STAT 502 and matrix algebra	3 credits
Applied Multivariate Statistical Analysis	STAT 505	Analysis of multivariate data; T2-tests; particle correlation; discrimination; MANOVA; cluster analysis; regression; growth curves; factor analysis; principal components; canonical correlations. Prerequisites: STAT 501, STAT 502	3 credits
Sampling Theory and Methods	STAT 506	Theory and application of sampling from finite populations. Prerequisites: calculus, 3 credits in statistics (STAT 500 is recommended)	3 credits
Epidemiologic Research Methods	STAT 507	Research and quantitative methods for analysis of epidemiologic observational studies. Non-randomized, intervention studies for human health, and disease treatment. Prerequisites: 3 credits in statistics, STAT 250 or equivalent	3 credits
Applied Data Mining and Statistical Learning	STAT 508	Data Mining tools are exploring data with regression, PCA, discriminate analysis, cluster analysis, classification and regression trees (CART). Prerequisites: STAT 501 or a similar course that covers analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression	3 credits
Design and Analysis of Clinical Trials	STAT 509	An introduction to the design and statistical analysis of randomized and observational studies in biomedical research. Prerequisites: STAT 500	3 credits
Applied Time Series Analysis	STAT 510	Identification of models for empirical data collected over time. Use of models in forecasting. Prerequisites: STAT 462, STAT 501, or STAT 511	3 credits
Statistical Analysis of Genomics Data	STAT 555	Statistical Analysis of High Throughput Biology Experiments.	3 credits

Course Availability

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