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Master of
Applied Statistics

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.

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.

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.

Required Courses (15 credits)

  • 3
    credits

    Probability spaces, discrete and continuous random variables, transformations, expectations, generating functions, conditional distributions, law of large numbers, central limit theorems.

    • Prerequisite

      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 and linear algebra (similar to MATH 220). If taken more than a few years ago, students are strongly encouraged to review their calculus knowledge.

  • 3
    credits

    A theoretical treatment of statistical inference, including sufficiency, estimation, testing, regression, analysis of variance, and chi-square tests.

    • Prerequisite

      STAT 414

  • 3
    credits

    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 principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs.

    • Prerequisite

      STAT 462 or STAT 501

  • 2
    credits

    General principles of statistical consulting and statistical consulting experience. Preparation of reports, presentations, and communication aspects of consulting are discussed.

    • Prerequisite

      STAT 502 and at least one of STAT 503, STAT 504, or STAT 506.

  • 1
    credit

    Statistical consulting experience including client meetings, development of recommendation reports, and discussion of consulting solutions.

    • Prerequisite

      STAT 580

Elective Courses (15 credits)

  • 3
    credits

    Tests based on nominal and ordinal data for both related and independent samples. Chi-square tests, correlation.

    • Prerequisite

      STAT 200, STAT 220, STAT 240, STAT 250, STAT 301, or STAT 401

  • 1
    credit

    Selection and evaluation of statistical computer packages.

    • Prerequisite

      3 credits in statistics

  • 1
    credit

    Intermediate SAS for data management.

    • Prerequisite

      STAT 480

  • 1
    credit

    This course covers advanced statistical procedures in SAS, including ANOVA, GIM, CORR, REG, MANOVA, FACTOR, DISCRIM, LOGISTIC, MIXED, GRAPH, EXPORT, and SQL.

    • Prerequisite

      STAT 480 and STAT 481

  • 3
    credits

    Introduction, intermediate, and advanced topics in SAS.

    • Prerequisite

      3 credits in statistics

    • Note

      Credit cannot be received for both STAT 483 and STAT 480/481/482.

  • 1
    credit

    Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics.

  • 1
    credit

    Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics. R is a popular tool for statistical analysis and research used by a growing number of data analysts inside corporations and academia.

    • Enforced Concurrent at Enrollment

      STAT 484

  • 2
    credits

    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

  • 3
    credits

    Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics.

    • Prerequisite

      one undergraduate course in statistics

  • 3
    credits

    Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs.

    • Prerequisite

      STAT 501 or STAT 502

  • 3
    credits

    Models for frequency arrays; goodness-of-fit tests; two-, three-, and higher- way tables; latent and logistic models.

  • 3
    credits

    Analysis of multivariate data; T2-tests; particle correlation; discrimination; MANOVA; cluster analysis; regression; growth curves; factor analysis; principal components; canonical correlations.

    • Prerequisite

      STAT 501, STAT 502

  • 3
    credits

    Theory and application of sampling from finite populations.

    • Prerequisite

      calculus, 3 credits in statistics (STAT 500 is recommended)

  • 3
    credits

    Research and quantitative methods for analysis of epidemiologic observational studies. Non-randomized, intervention studies for human health, and disease treatment.

    • Prerequisite

      3 credits in statistics, STAT 250 or equivalent

  • 3
    credits

    Data mining tools are exploring data with regression, PCA, discriminate analysis, cluster analysis, and classification and regression trees (CART).

    • Prerequisite

      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

    The objective of the course is to introduce students to the various design and statistical analysis issues in biomedical research. This is intended as a survey course covering a wide variety of topics in clinical trials, bioequivalence trials, toxicological experiments, and epidemiological studies.

    • Prerequisite

      STAT 500

  • 3
    credits

    Identification of models for empirical data collected over time. Use of models in forecasting.

    • Prerequisite

      STAT 462 or STAT 501 or STAT 511

  • 3
    credits

    Statistical Analysis of High Throughput Biology Experiments.

Course Availability

If you're ready to see when your courses will be offered, visit our public LionPATH course search (opens in new window) to start planning ahead.

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