Penn State's 12-credit online Graduate Certificate in Applied Statistics consists of required and elective courses that can deepen your knowledge of statistical analysis and provide you with:
- training on SAS and Minitab software packages
- a blend of practical and theoretical data-analysis skills
- sophisticated tools and knowledge to handle and analyze data
The graduate certificate allows you to simultaneously gain graduate credit and a highly valued skill set. The applied statistics program is designed as a "stand-alone" certificate or can serve as a "step-up" program into a master's degree — including the Master of Applied Statistics degree.
To earn the Graduate Certificate in Applied Statistics, you will take 6 credits of required courses and choose 6 credits of electives based on your professional goals. At least 6 of the 12 credits must come from courses at the 500 level or above. A minimum 3.0 GPA is required to obtain the certificate.
Course List - Graduate Certificate in Applied Statistics
| Title | Abbreviation | Description | Credits |
|---|---|---|---|
| Applied Statistics | STAT 500 | Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics. Prerequisite: 3 credits in statistics |
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; matrix algebra |
3 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 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 |
| 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 |
| Introduction to SAS | STAT 480 | Selection and evaluation of statistical computer packages. Prerequisite: 3 credits in statistics |
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 can not be received for both STAT 483 and STAT 480/481/482. Prerequisites: 3 credits in statistics |
3 credits |
| The R Statistical Programing Language | STAT 484 | Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics. | 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. R is a popular tool for statistical analysis and research used by a growing number of data analysts inside corporations and academia. | 1 credit |
| Analysis of Variance and Design of Experiments | STAT 502 | Design principles; optimality; confounding in split-plot, repeated measures, fractional factorial, response surface, and balanced/partially balanced incomplete block designs. Prerequisites: STAT 462 or STAT 501 |
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 462 or 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 logistics models. Prerequisites: STAT 500, 501, and 502; matrix algebra |
3 credits |
| Applied Multivariate Statistical Analysis | STAT 505 | Analysis of multivariate data; T-squared tests; partial correlation; discrimination; MANOVA; cluster analysis; regression; growth curves; factor analysis; principal components; canonical correlations. Prerequisites: STAT 501 and 502; matrix algebra |
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 |
| Epidemiology Research Methods | STAT 507 |
Develops research and quantitative methods related to the design and analysis of epidemiological (mostly observational) studies. Such studies assess the health and disease status of one or more human populations or identify factors associated with health and disease status. To a lesser degree, the course also covers non-randomized, intervention (experimental) studies that may be designed and analyzed with epidemiological methods. This course is a second-level course and complements Biostat Methods, STAT 509, which is focused on clinical (experimental) trials. Together, these two courses provide students with a complete review of research methods for the design and analysis for common studies related to human health, disease, and treatment. Prerequisite: STAT 500 |
3 credits |
| Design and Analysis of Clinical Trials | STAT 509 |
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 |
| Applied Time Series Analysis | STAT 510 | Identification of models for empirical data collected over time. Use of models in forecasting. Prerequisite: STAT 462, 501, or 511 |
3 credits |
| Applied Data Mining and Statistical Learning | STAT 897 | Data Mining tools are exploring data with regression, PCA, discriminate analysis, cluster analysis, 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 |
Course Availability
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