Statistics

Concepts of population and sample, data collection, descriptive statistics and exploratory data analysis, frequency distributions, basic probability concepts, random variables, discrete and continuous probability models and their applications, central limit theorem and its applications and an introduction to statistical inference. NOTE: Credit can be obtained for only one of STAT 1793, STAT 2263, STAT 2593, BA 1605, PSYC 2901.

An introductory course in statistics. Experiments, sampling, basic descriptive statistics. Probability, random variables, Normal distribution. Confidence intervals for means and proportions. Tests of hypotheses. Paired samples vs. two independent samples. Contingency tables. Regression, correlation. Introduction to analysis of variance. Examples drawn from the health sciences. Use of a statistical computer package. NOTE: Credit can be obtained for only one of STAT 1793, STAT 2263, STAT 2593, BA 1605, PSYC 2901.

The first half of a two-part sequence covering various topics in probability and statistics. This course provides an introduction to probability theory and the theory of random variables and their distributions. Probability laws. Discrete and continuous random variables. Means, variances and moment generating functions. Sums of random variables. Joint discrete distributions. Central Limit Theorem. Examples drawn from engineering, science, computer science and business. 

Multivariate normal distribution; multivariate regression and the analysis of variance; canonical correlations; principal components; classification procedures; factor analysis; computer applications. Students should have some exposure to matrix algebra.

Course will include random number generation, simulation of random variables and processes, Monte Carlo techniques and integral estimation, the computation of percentage points and percentiles, as well as resampling methods.

Simple and multiple linear regression, least squares estimates and their properties, tests of hypotheses, F-test, general linear model, prediction and confidence intervals. Orthogonal and non-orthogonal designs. Weighted least squares. Use of a statistical computer package. NOTE: Credit can be obtained for only one of STAT 4703, and ECON 4645. 

The second half of a two part sequence covering various topics in probability and statistics. This course provides and introduction to essential techniques of statistical inference. Samples and statistics versus populations and parameters. Distributions of functions of random variables. Sampling from the normal distribution. The t and F distributions. Point estimation by the method of moments and maximum likelihood estimation. Methods of evaluating point estimators. Finding and evaluating hypothesis tests and confidence intervals. Brief introduction to method of moments and maximum likelihood. Tests and intervals for means, variances, and proportions (one and two sample). Regression models. Examples drawn from engineering, science, computer science, and business.

Research project in Statistics carried out by the student under the supervision of a member of the Department. The student will submit a written report and make an oral presentation.