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.
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
| 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 |
| 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 | 1 credit |
| 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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