Statistics & Biostatistics Course Descriptions
16:960:563 Regression Analysis (3)
Review of basic statistical theory and matrix algebra; general regression models, computer application to regression techniques, residual analysis, selection of regression models, response-surface methodology, nonlinear regression models, experimental-design models, analysis of covariance. Emphasis on applications.
16:960:580 Basic Probability (3)
Discrete-probability spaces, combinatorial analysis, occupancy and matching problems, basic distributions, probabilities in a continuum; random variables, expectations, distribution functions, conditional probability and independence; coin tossing, weak law of large number, deMoivre-Laplace theorem. Note: 16:960:582 may be substituted in place of 16:960:580. Credit will not be given for both 580 and 582.
16:960:583 Methods of Inference (3)
Theory of point and interval estimation and hypothesis testing. Topics include sufficiency, unbiasedness, and power functions. Emphasis on application of the theory in the development of statistical procedures.
16:960:590 Design of Experiments (3)
Fundamental principles of experimental design; completely randomized variance component designs, randomized blocks, Latin squares, incomplete blocks, partially hierarchic mixed-model experiments, factorial experiments, fractional factorials, response surface exploration.
16:960:586 Interpretation of Data (3)
Modern methods of data analysis with an emphasis on statistical computing: univariate statistics, data visualization, linear models, generalized linear models (GLM), multivariate analysis and clustering methods, tree-based methods, and robust statistics. Expect to use statistical software packages, such as SAS (or SPSS) and Splus (or R) in data analysis. Prerequisite: Level IV statistics. Recommended: 16:960: 563.
16:960:553 Categorical Data Analysis (3)
Two-by-two frequency tables, Fisher's exact test, measures of association, general contingency tables, loglinear models, logistic regression, repeated categorical-response data, maximum likelihood estimation, tables with ordered categories, discriminant analysis. Prerequisite: Level V statistics or permission of instructor.
16:960:542 Life Data Analysis (3)
Statistical methodology for survival and reliability data. Topics include life-table techniques; competing risk analysis; parametric and nonparametric inferences of lifetime distributions; regressions and censored data; Poisson and renewal processes; multistate survival models and goodness-of-fit test. Statistical software used. Prerequisites: One year of calculus, Level V statistics, or permission of instructor.
16:960:540 Statistical Quality Control I (3)
Construction and analysis of control charts for variables and attributes; histogram analysis; use and evaluation of Dodge-Romig and Military Standards acceptance sampling plans. Prerequisites: Level IV statistics, 16:960:582 or equivalent.
16:960:541 Statistical Quality Control II (3)
Introduction to state-of-the-art methods in statistical quality control, including economic design and Bayesian methods in process control, Taguchi's method and statistical tolerance. Prerequisites: 16:960:540, 590.
16:960:545 Statistical Practice (3)
Objectives of statistical collaboration, problem definition, formation of solutions, active consultation, tools of statistical practice, searching literature, data collection form design, codebook development, data entry and cleaning, documentation and presentation of statistical analysis. Prerequisite: Level IV statistics.
16:960:554 Applied Stochastic Processes (3)
Markov chains; recurrence; random walk; gambler's ruin; ergodic theorem and stationary distribution; continuous time Markov chains; queuing problems; renewal processes; martingales; Markov processes; Brownian motion; concepts in stochastic calculus; Ito's formula. Prerequisites: Advanced calculus, 16:960:582 or equivalent.
16:960:555 Nonparametric Statistics (3)
Introduction and survey of distribution-free approaches to statistical inference. Fisher's method of randomization, distribution-free test procedures for means, variances, correlations, and trends; rank tests; relative efficiency, asymptotic relative efficiency, and normal-score procedures; binomial, hypergeometric distributions, and combina-torial run theory. Also, tests of goodness-of-fit, including the Kolmogorov-Smirnov and chi-square tests, contingency-table analysis, tolerance sets, and Tchebycheffe-type inequalities. Emphasis on applications. Prerequisites: Level IV statistics, 16:960:582, or permission of instructor.
16:960:565 Applied Time Series Analysis (3)
Model-based forecasting methods, autoregressive and moving average models, ARIMA, ARMAX, ARCH, state-space models, estimation, forecasting and model validation, missing data, irregularly spaced time series, parametric and nonparametric bootstrap methods for time series, multiresolution analysis of spatial and time-series signals, time-varying models and wavelets. Prerequisite: Level V statistics or permission of instructor.
16:960:567 Applied Multivariate Analysis (3)
Methods of reduction of dimensionality, including principal components, factor analysis, and multidimensional scaling; correlation techniques, including partial, multiple, and canonical correlation; classification and clustering methods. Emphasis on data-analytic issues, concepts, and methods (e.g., graphical techniques) and on applications drawn from several areas, including behavioral management and physical and engineering sciences. Prerequisite: Level V statistics or permission of instructor.
16:960:575 Acceptance Sampling Theory (3)
Selection, operation, and statistical behavior of sampling plans. Dodge-Romig plans; continuous, chain, and skip-lot plans; variable sampling plans. Economic analysis and study of sampling systems. Prerequisite: Level IV statistics.
16:960:576 Survey Sampling (3)
Introduction to the design, analysis, and interpretation of sample surveys. Sampling types covered include simple random, stratified random, systematical, cluster, and multistage. Methods of estimation described to estimate means, totals, ratios, and proportions. Development of sampling designs combining a variety of types of sampling and methods of estimation, and detailed description of sample size determinations to achieve goals of desired precision at least cost. Prerequisite: 16:960:582 or equivalent.
19:960:584 Biostatistics I-Observational Studies (3)
Statistical techniques for biomedical data. Analysis of observational studies emphasized. Topics include measures of disease frequency and association; inferences for dichotomous and grouped case-control data; logistic regression for identification of risk factors; Poisson models for grouped data; bioassay. SAS used in analysis of data. Prerequisites: One year of calculus and Level V statistics.
19:960:585 Biostatistics II-Clinical Trials (3)Statistical and practical design, conduct, and analysis of controlled clinical experiments. Topics include introduction to phases of clinical trials; power and sample size estimation; randomization schemes; study design; human subject considerations and recruitment; data collection design and process; data monitoring and interim analysis; baseline covariate adjustment and data analysis; writing and presenting results. Standard statistical software used for randomization, power/sample size estimation and data analysis. 16:960:584 Biostatistics I is not required. Prerequisite: Level IV statistics.
16:960:587 Interpretation of Data II (3)
Modern methods of data analysis and advanced statistical computing techniques: smooth regression (including GAM models), nonlinear models, Monte-Carlo simulation methods, the EM algorithm, MCMC methods, spatial statistics, longitudinal data analysis/mixed effects models/GEE, latent variable models, hidden Markov models, Bayesian methods, etc. Expect to use the statistical software package Splus (or R) and to do some Splus (or R) programming for data analysis. Prerequisite: 16:960:586 or permission of instructor.
16:960:588 Data Mining (3)
Databases and data warehousing, exploratory data analysis and visualization, an overview of data mining algorithms, modeling for data mining, descriptive modeling, predictive modeling, pattern and rule discovery, text mining, Bayesian data mining, observational studies. Prerequisites: 16:960:567, 587, or permission of instructor.
16:960:591 Advanced Design of Experiments (3)
Strategy of experimentation, screening designs, factorial designs, response surface methodology, evolutionary operation, mixture designs, incomplete blocking designs, computer-aided experimental designs, and design optimality criteria. Prerequisite: 16:960:590. Recommended: 16:960:563.