Course List - Graduate Certificate in Applied Statistics
| Required Courses (6 credits) | ||
| STAT 500 | Applied Statistics 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 |
| STAT 501 | Regression Methods 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 |
From the following list, choose the courses that will help you best to meet your goals.
| Elective Courses (6 credits) | ||
| STAT 414 | Introduction to Probability Theory
Probability spaces, discrete and continuous random variables, transformations, expectations, generating functions, conditional distributions, law of large numbers, central limit theorems. |
3 credits |
| STAT 415 | Introduction to Mathematical Statistics A theoretical treatment of statistical inference, including sufficiency, estimation, testing, regression, analysis of variance, and chi-square tests. Prerequisites: STAT 414 |
3 credits |
| STAT 480 | Introduction to SAS Selection and evaluation of statistical computer packages. Prerequisite: 3 credits in statistics |
1 credit |
| STAT 481 | Intermediate SAS for Data Management Intermediate SAS for data management. Prerequisite: STAT 480 |
1 credit |
| STAT 482 | Advanced Statistical Procedures in SAS 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 |
| STAT 483 | Statistical Analysis System Programming 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 |
| STAT 497C | Topics in Stat Computing with R 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) Reptitive 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 |
| STAT 502 | Analysis of Variance and Design of Experiments Analysis of variance and design concepts; factorial, nested, and unbalanced data; ANCOVA; blocked, Latin square, split-plot, repeated measures designs. Prerequisite: STAT 462 or STAT 501 |
3 credits |
| STAT 503 | Design of Experiments 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 |
| STAT 504 | Analysis of Discrete Data Models for frequency arrays; goodness-of-fit tests; two-, three-, and higher-way tables; latent and logistics models. Prerequisites: STAT 500 and 501; matrix algebra |
3 credits |
| STAT 505 | Applied Multivariate Statistical Analysis 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 |
| STAT 506 | Sampling Theory and Methods Theory and application of sampling from finite populations. Prerequisites: calculus; 3 credits in statistics |
3 credits |
| STAT 507 | Epidemiology Research Methods Research and quantitative methods for analysis of epidemiologic observational studies. Non-randomized, intervention studies for human health, and disease treatment. Prerequisite: STAT 250 or equivalent |
3 credits |
| STAT 509 | Design and Analysis of Clinical Trials An introduction to the design and statistical analysis of randomized and observational studies in biomedical research. Prerequisite: STAT 500 |
3 credits |
| STAT 510 | Applied Time Series Analysis Identification of models for empirical data collected over time. Use of models in forecasting. Prerequisite: Stat 462, 501, or 511 |
3 credits |
| STAT 557 | Data Mining I This course introduces data mining and statistical/machine learning, and their applications in information retrieval, database management, and image analysis. |
3 credits |