## Option-A Minor in Statistics for Graduates

## General Requirements for an Option-A Minor in Statistics for Graduates:

Please carefully read the requirements below. Requests for further information should be addressed to the faculty member acting as Minors Advisor in the Statistics Department. **Note:** Candidates for an Option-A Minor in Statistics must be aware of the Graduate School "Minors" policy [1].

## Statistics Minor Option for Graduates

- For admission for an Option-A Minor in Statistics, the candidate must have had at least one year of calculus, and an introductory knowledge of Statistics that is satisfactory to the Department. Stat 224, 301, 302, 324, 371, 571 or an equivalent course is sufficient for this purpose.
- Students must take at least 4 courses acceptable for the minor totaling at least 12 credits. Courses acceptable for the minor are: Stat 309, 310, 311, 312, 327, 333, 349, 351, 411, 421, 424, 456, 461, 471, 479, 542, 572, 575, 609, 610, 641, 642, 679, 709, 710, 732, 741, 760, 761, 771, 775, 803, 809, 811, 834, 840, 841, 849, 850, 851, 860, 877, 992. Besides these courses, up to three credits from the following list are acceptable for the minor: Math 431, 475, 632, 833, or CS 525 or 726, or some other course in the University of suitable statistical content if approved by the Minor Program Advisor in the Department of Statistics. A student can include at most one of 309, 609, and 709, and at most one of 310, 610, and 710. The courses taken by a particular student should depend on the student’s major field or individual needs.
- The student should have a program of study
**approved**by the Minor Program Advisor in the Department of Statistics and the student's major professor,**early in the student's graduate work**. The proposed program must be submitted to and approved by the Minor Program Advisor in Statistics**upon, or before, the completion of six credits.** - The student must achieve a 3.00 GPA in courses used to satisfy the minor requirement.

**COURSES IN STATISTICS**

- Stat 309 & 310 - Introduction to Mathematical Statistics (for majors in Mathematics & Statistics)
- Stat 311 & 312 - Introduction to Mathematical Statistics (for majors in Engineering, the Natural, Agricultural and Life Sciences)
- Stat 327 – Learning a Statistical Language [soon to be 303, 304, 305. ]
- Stat 333 - Applied Regression Analysis
- Stat 349 - Introduction to Time Series
- Stat 351 - Introductory Non-Parametric Statistics
- Stat 411 - An Introduction to Sample Survey Theory and Methods
- Stat 421 - Applied Categorical Data Analysis
- Stat 424 - Statistical Experimental Design
- Stat 456 - Applied Multivariate Analysis
- Stat 461 – Financial Statistics
- Stat 471 - Introduction to Computational Statistics
- Stat 479 - Special Topics in Statistics
- Stat 542 - Introduction to Clinical Trials
- Stat 572 - Statistical Methods for Bioscience
- Stat 575 – Statistical Methods for Spatial Data
- Stat 609 & 610 - Mathematical Statistics (MS level)
- Stat 641 - Statistical Methods for Clinical Trials
- Stat 642 - Statistical Methods for Epidemiology
- Stat 679 - Special Topics in Statistics
- Stat 701 - Applied Time Series Analysis -Forecasting and Control
- Stat 709 & 710 - Mathematical Statistics (Ph.D. level)
- Stat 732 - Large Sample Theory of Statistical Inference
- Stat 741 - Survival Analysis Theory & Methods
- Stat 760 - Multivariate Analysis I
- Stat 761 - Decision Trees for Multivariate Analysis
- Stat 771 - Statistical Computing
- Stat 775 - Introduction to Bayesian Decision and Control
- Stat 803 - Experimental Design
- Stat 809 - Nonparametric Statistics
- Stat 811 - Sample Survey: Theory and Methodology
- Stat 834 – Empirical Processes and Semiparametric Inference
- Stat 840 - Statistical Model Building and Learning
- Stat 841 - Nonparametric Statistics and Machine Learning Methods
- Stat 849 & 850 - Analysis of Variance
- Stat 851 - Generalized Linear Models
- Stat 860 - Estimation of Functions From Data
- Stat 877 – Statistical Methods for Molecular Biology
- Stat 992 - Seminar

**COURSES JOINTLY LISTED IN STATISTICS & MATHEMATICS OR COMPUTER SCIENCE**

- Math 431 - Introduction to the Theory of Probability
- Math 475 - Introduction to Combinatorics
- Math 632 - Introduction to Stochastic Processes
- Math 733 – Theory of Probability I
- Math 734 – Theory of Probability II
- Math 833 - Topics in Probability
- CS 525 - Linear Programming Methods
- CS 726 - Non-linear Programming Methods