Statistics

Graphical and numerical descriptive measures. Simple linear regression. Basic probability concepts, random variables, sampling distributions, central limit theorem. Large and small sample confidence intervals and significance tests for parameters associated with a single population and for comparison of two populations. Use of statistical computer software and computer applets to analyze data and explore new concepts. (M) GR-M†

Basic statistical concepts presented in a conceptual fashion, emphasizing data collection and analysis rather than theory. Topics include exploratory data analysis, design of surveys and experiments, introduction to estimation and significance tests and the use of statistics in the social sciences and the media. (M) GR-M†

An introduction to the analysis of variance. Nonparametric statistical methods and applications. Analysis of count data: chi-square and contingency tables. Simple and multiple linear regression methods with applications. (M) GR-M†

A survey of the basic concepts in probability and statistics with engineering applications. Topics include probability, discrete and continuous random variables, estimation, hypothesis testing, and linear and multiple regression. (M) GR-M†

Computer simulations on simple statistical techniques such as histograms, z-tests and t-tests, analyzing large data sets by regression, contingency tables, non-parametric and simple multivariate procedures. (M) GR-M†

Introduces statistical design and analysis techniques needed to perform pharmaceutical research and evaluate articles in medical literature. Designing epidemiologic and clinical studies, evaluating diagnostic testing procedures, interpreting the use of rates in medical literature, and using frequently used statistical methods of data analysis. Emphasis will be on concepts and their application to critical appraisal of statistical contents in medical literature. (M) GR-M†

Specialized statistical methods in biological medical sciences. Contents include analysis of rates and proportions, statistical methods in biological assay, analysis of survival data, planning and designing of clinical trials. (M) GR-M†

STA 4210 Regression Analysis.

Simple linear regression and multiple linear regression models. Inference about model parameters and predictions, diagnostic and remedial measures about the model, independent variable selection, multicolinearity, autocorrelation, and nonlinear regression. SAS implementation of the above topics. (M) GR-M†

An introduction to the basic principles of experimental design: analysis of variance for experiments with a single factor; randomized blocks and Latin square designs: multiple comparison of treatment means; factorial and nested designs; analysis of covariance; response surface methodology. GR-M†

An introduction to the design of sample surveys and the analysis of survey data, the course emphasizes practical applications of survey methodology. Topics include sources of errors in surveys, questionnaire construction, simple random, stratified, systematic and cluster sampling, ratio and regression estimation, and a selection of special topics such as applications to quality control and environmental science. (M) GR-M†

Introduction to the theory of probability, counting rules, conditional probability, independence, additive and multiplicative laws, Bayes Rule. Discrete and continuous random variables, their distributions, moments, moment generating functions. Multivariate probability distributions, independence, covariance. Distributions of functions of random variables. (M) GR-M†

Sampling distributions, central limit theorem, estimation, properties of point estimators, confidence intervals, hypothesis testing, common large sample tests, normal theory small sample tests, uniformly most powerful and likelihood ratio tests, linear models and least squares, correlation. Introduction to analysis of variance. (M) GR-M†

Introduction to nonparametric statistics, including one- and two-sample testing and estimation methods, one- and two-way layout models and correlation and regression models.

Description and inference using proportions and odds ratios, multi-way contingency tables, logistic regression and other generalized linear models, loglinear models applications.

Philosophy and tools of total quality management, design of experiments for process optimization via response surface methods including factorial, fractional factorial, Plackett-Burman and central composite designs; control chart methods, including Shewhart and cusum charts.

Review of matrix theory, univariate normal, t, chi-squared and F distributions and multivariate normal distribution. Inference about multivariate means including Hotelling’s T2, multivariate analysis of variance, multivariate regression and multivariate repeated measures. Inference about covariance structure including principal components, factor analysis and cannonical correlation. Multivariate classification techniques including discriminant and cluster analyses. Additional topics at the discretion of the instructor, time permitting.

Theoretical development of elementary stochastic processes, including Poisson processes and their generalizations, Markov chains, birth and death processes, branching processes, renewal processes, queuing processes and genetic and ecological processes. GR-M†

Special topics designed to meet the needs and interests of individual students. GR-M†

Rotating topics designed to meet the needs and interests of individual students. GR-M†

Supervised activity associated with planning and/or analyzing data from a research project. Permission of the undergraduate coordinator required. Supervision by a faculty member or delegated authority and a post-internship written report are required. S/U