Biostatistics Courses
Sta 550 Introduction to Computing (1)
An introduction to the use of micro and mainframe computers. Communications between computers and the use of statistical and word processing software packages will be included.
Sta 552 (Epi 552) Principles of Statistical Inference I (3)
An introduction to descriptive statistics, measures of central tendency and variability, probability distributions, sampling estimation, confidence intervals and hypothesis testing. Computing will be introduced and used throughout the course. Sta 552 and Sta 553 will satisfy the core requirement in statistics for programs in the School of Public Health.
Sta 553 (Epi 553) Principles of Statistical Inference II (3)
Continuation of Sta 552. Topics will include correlation, regression, analysis of contingency tables and non-parametric statistics. Computing will be used throughout the course. Sta 552 and Sta 553 will satisfy the core requirement in statistics for programs in the School of Public Health. Prerequisite: Sta 552 or equivalent.
Sta 554 (Mat 554) Introduction to the Theory of Statistics I (3)
A mathematical treatment of principles of statistical inference. Topics include probability, random variables and random vectors, univariate and multivariate distributions and an introduction to estimation. Appropriate for graduate students in other disciplines and for preparation for the second actuarial examination. Prerequisites: Calculus and linear algebra.
Sta 555 (Mat 555) Introduction to the Theory of Statistics II (3)
Continuation of Sta 554. Topics include methods of estimation, theory of hypothesis testing, sufficient statistics, efficiency and linear models. Appropriate for graduate students in other disciplines and for preparation for the second actuarial examination. Prerequisite: Sta 554 or equivalent.
Sta 556 (Mat 556) Introduction to Bayesian Inference I (3)
Topics include subjective probability, axiomatic development and applications of utility, basic concepts of decision theory, conjugate and locally uniform prior distributions. Prerequisite: Sta 555 or equivalent.
Sta 557 (Mat 557) Introduction to Bayesian Inference II (3)
Continuation of Sta 556. Topics will include limiting posterior distributions, estimation and hypothesis testing, preposterior distributions and their application to the design of statistical investigations. Prerequisite: Sta 556 or equivalent.
Sta 558 (Mat 558) Methods of Data Analysis I (3)
Statistical methodology emphasizing exploratory approaches to data. Elementary notions of modeling and robustness. Overview of inferential techniques in current use. Criteria for selection and applications methods. Use of computing facilities to illustrate and implement methods. Regression and analysis of variance are the primary topics. Prerequisite: Minimum requirement: contents of Sta 552.
Sta 559 (Mat 559) Methods of Data Analysis II (3)
Continuation of Sta 558. Topics will include clustering, multivariate analyses, sequential and nonparametric methods. Prerequisite: Sta 558 or equivalent.
Sta 560 (Mat 560) Introduction to Stochastic Processes I (3)
An introduction to applied stochastic processes. Topics include Markov chains, queuing theory, renewal theory, Poisson processes and extensions, epidemic and disease models. Prerequisite: Sta 555 or an introductory probability course.
Sta 561 Introduction to Stochastic Processes II (3)
Continuation of Sta 560. More advanced topics in Markov chains, queuing theory, Poisson processes and extensions, epidemic and disease models. Prerequisite: Sta 560 or permission of the instructor.
Sta 562 Design of Experiments I (3)
Principles in the design and analysis of controlled experiments. Topics include general linear hypotheses, multiple classifications, Latin squares and factorial designs. Prerequisite: Sta 555 or equivalent.
Sta 563 Design of Experiments II (3)
Continuation of Sta 562. More advanced designs, information and efficiency, and introduction to response surface methodology. Prerequisite: Sta 562 or equivalent.
Sta 564 Sample Survey of Methodology I (3)
Principles of survey sampling and analysis. Topics include simple random sampling, stratified sampling, cluster sampling and multistage sampling. Prerequisite: Sta 555 or equivalent.
Sta 565 Sample Survey Methodology II (3)
Continuation of Sta 564. Topics include more complex designs in stratified sampling, cluster sampling and multistage sampling. A introduction to cost studies, nonsampling errors and miscellaneous topics. Prerequisite: Sta 564 or equivalent.
Sta 566 Analysis of Categorical Data I (3)
Introduction to the analysis of categorical data. Topics include rates, ratios and proportions, relative risk, Cochran-Mantel Haenszel procedures, linear and log-linear models for categorical data, maximum likelihood estimation and tests of hypotheses. Prerequisite: Sta 555 or equivalent.
Sta 567 Analysis of Categorical Data II (3)
Continuation of Sta 566. Topics will include more complex linear and log- linear models for categorical data, goodness of fit measures and tests of hypotheses. Prerequisite: Sta 566 or equivalent.
Sta 568 Statistical Ecology (3)
Density estimates for closed and open populations using simple and multiple marking methods. Mortality and survival estimation, population dynamics. Spatial patterns in one and two-species populations. Characterization of many-species populations. Prerequisite: Sta 555 or equivalent.
Sta 569 (Bio 540) Principles of Bioinformatics (3)
In this course, you will learn basic programming skills that will allow you to analyze biological data sets with a focus on next-generation sequencing data sets. This course focuses on Unix shell and Python programming skills, and applies them to basic problem sets relating to parsing large files, e.g. data that is generated by next generation sequencing. Students will also learn how to manage computer resources and work in a shared user environment (HPC). Prerequisites: Bio 524 or permission of instructor.
Sta 570 Topics in Evaluation (3)
Selected topics in experimental design, data collection instruments, registries, outcome measures, assessment of agencies and/or programs and qualitative and quantitative methodologies used in evaluation. Prerequisite: Consent of instructor.
Sta 571 Topics in Informatics (2-3)
Selected topics in informatics, including medical informatics, information systems, wide area networks, storing, retrieving and analyzing of medical and public health information. Prerequisite: Consent of instructor.
Sta 572 Introductory Applied Statistics for Environmental and Biomedical Health Sciences (2)
This introductory course will familiarize students with basic applied statistical methods used in data analysis for laboratory-based studies. Real world examples will be used to illustrate the application of these methods. The course is designed for students from various natural sciences who have no background in statistics.
Sta 573 Introductory Workshop on Bioinformatics (2)
This is a 4-day introductory workshop in bioinformatics for participants coming from either biological or quantitative disciplines. This course serves as “Bioinformatics in a nut-shell” for those interested in learning about this new field. Fundamental concepts in searching biological databases will be demonstrated. Participants will gain hands-on experience in performing BLAST searches and other advanced database searches.
Sta 610 Statistical Analysis with Missing Data (3)
The overall goal of this course is to develop a broad and thorough working knowledge of the missing data techniques at a practical, conceptual and mathematical level. The students are expected to gain hands-on experience applying these techniques in many settings commonly encountered in public health and biostatistics. Prerequisites: Data Analysis I and II; Mathematical Statistics or with instructor’s permission.
Sta 650 Advanced Topics in Bioinformatics (3)
This is an advanced graduate-level course in bioinformatics. Topics covered include UNIX-based computer skills, machine learning algorithms, prediction of protein subcellular localization, pathway reconstruction, phylogenetic analysis, protein structure alignment and analysis, microarray data analysis, clustering methods and computational proteome analysis. This course also includes hands-on lab sessions. Prerequisite: Bio 540.
Sta 654 Probability and Theory of Statistical Inference I (3)
Univariate and multivariate distribution theory, properties of estimators, large sample theory, confidence intervals and theory of tests. Prerequisite: Sta 555 or equivalent.
Sta 655 Probability and Theory of Statistical Inference II (3)
Continuation of Sta 654. Advanced theory of test, decision theory and other topics. Prerequisite: Sta 654 or equivalent.
Sta 656 Design of Clinical Trials (3)
Introduction to topics in the design and analysis of clinical trials and related experiments. Prerequisite: Sta 555 or equivalent.
Sta 657 Mathematical Models in Demography (3)
Introduction to mathematical methods and application required for natality models, deterministic and stochastic models for population growth. Prerequisite: Sta 555 or equivalent.
Sta 658 Mathematical Models in Biometry I (3)
Topics in the mathematical and statistical methods required to model deterministic and stochastic models for phenomenon found in the different areas of biometry and the health sciences. Prerequisite: Sta 555 or equivalent.
Sta 659 Mathematical Models in Biometry II (3)
Continuation of Sta 658. Advanced topics in the mathematical and statistical methods required to model deterministic and stochastic models for phenomenon found in the different areas of biometry and the health sciences. Prerequisite: Sta 658 or consent of the instructor.
Sta 660 (Mat 660) Linear Models I (3)
Topics include the theory of least squares, distribution of quadratic forms, G-inverse, general Gauss-Markov model, estimation, hypothesis tests, confidence intervals for unrestricted and restricted models, regression models and analysis of variance. Prerequisite: Sta 555 or equivalent.
Continuation of Sta 660. Topics include advanced analysis of variance and analysis of covariance, repeated measures, mixed and random models. Prerequisite): Sta 660 or equivalent.
Sta 662 (Mat 662) Multivariate Analysis I (3)
Topics include the basic properties of multivariate normal distributions and other related distributions, inference in multivariate cases and principle component analysis. Prerequisite: Sta 555 or the consent of the instructor.
Sta 663 Multivariate Analysis II (3)
Continuation of Sta 662. Topics will include discriminate analysis, canonical correlation analysis and factor analysis. Prerequisite: Sta 662 or consent of the instructor.
Sta 664 (Mat 664) Time Series Analysis I (3)
Topics include the study of inference, estimation, prediction, parsimonious description for univariate time-ordered data, various models including Box-Jenkins and classical stationary processes with rational spectral densities. Prerequisite(s): Sta 555 and Sta 559 or consent of instructor.
Sta 665 (Mat 665) Time Series Analysis II (3)
Continuation of Sta 664. Advanced topics include the study of univariate and multivariate time-ordered data, various models including Box-Jenkins and classical stationary processes with rational spectral densities. Prerequisite: Sta 664 or consent of the instructor.
Sta 666 Survivorship Analysis I (3)
Topics in survival functions, hazard rates, life tables, estimation of survival functions from complete and censored data, fitting parametric models, tests of hypotheses, and covariate models. Prerequisite: Sta 555 or consent of instructor.
Sta 667 Survivorship Analysis II (3)
Continuation of Sta 666. Advanced topics in the theory of survival functions for complete and censored data, tests of hypotheses, and time dependent covariate models. Prerequisite: Sta 666 or consent of instructor.
Sta 668 Independent Study in Biostatistics (3)
Selected study of a topic in biostatistics. Prerequisite: Consent of instructor.
Sta 669 Master's Seminar in Biostatistics (3)
Selected topics in statistics. A report is written on the subject studies. Required of all candidates for a Master's Degree in Biostatistics, except those who write a master's thesis. Prerequisite: Consent of instructor.
Sta 670 Topics in Biostatistics (3)
Selected topics in biostatistics. Prerequisite: Consent of instructor.
Sta 697 Independent Study and Research (1-6)
Independent study and research at the master's level under the direction of a faculty member. May be repeated once for credit. Prerequisite: Consent of instructor. S/U graded.
Research leading to an acceptable master's thesis in biostatistics. May be repeated once for credit. Prerequisite: Consent of thesis director. S/U graded.
Sta 760 (Mat 760A) Probability Theory I (3)
Measure theoretical foundations of probability. Topics include: distribution functions and characteristic functions; classical limit laws; conditioning and martingales.
Sta 761 Probability Theory II (3)
Continuation of Sta 760. Prerequisite: Sta 760 or equivalent.
Sta 764 Advanced Time Series Analysis I (3)
Prerequisite: Sta 665 or consent of instructor.
Sta 765 Advanced Time Series Analysis II (3)
Prerequisite: Sta 764 or consent of instructor.
Sta 860 (Mat 860) Topics in Probability (1-4)
Selected topics and problems chosen from the area of probability. May be repeated for credit when topics differ. Prerequisite: Consent of instructor.
Sta 862 (Mat 862) Seminar in Probability (1-4)
Seminar organized to meet students' study and research requirements in the area of probability. Prerequisite: consent of instructor.
Sta 865 (Mat 865) Topics in Statistics (1-4)
Selected topics from the various areas of statistics. May be repeated for credit when the topics differ. Prerequisite: Consent of instructor.
Sta 867 (Mat 867) Seminar in Statistics (1-4)
Seminar organized to meet study and research requirements of students in biometry and statistics. Prerequisite: Consent of instructor.
Sta 868 Independent Study and Research in Biostatistics (2-5)
Independent study at the doctoral level under the direction of a member of the biostatistics faculty. May be repeated for credit. Prerequisite: Consent of instructor.
Sta 899 Doctoral Dissertation (1)
May be repeated for credit. Course grading is Load Only and does not earn credit. Prerequisite: Consent of dissertation director. Registration for this course is limited to doctoral students who have been admitted to candidacy.
