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.

Probability spaces: combinatorial probability, conditional probability, and independence. Random variables: discrete distributions, continuous distributions, expectation, variance, and covariance; linear combinations. Statistics: descriptive and graphical statistics; sampling; distributions. Inference: point estimation, confidence intervals; hypothesis tests; paired data designs; two sample inference, linear regression. NOTE: Credit can be obtained for only one of STAT 1793, STAT 2263, STAT 2593, BA 1605, PSYC 2901.

Concepts of estimation and test of hypothesis, sampling distributions, confidence interval estimation and test of hypothesis for proportion(s), mean(s) and standard deviation(s), association and trend analysis, elementary experimental designs and analysis of variance. NOTE: Credit can be obtained for only one of STAT 2793, BA 2606, PSYC 3913. 

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. 

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.

Experimental Design methods and theory, one-way and two-way classification models, split plot designs, incomplete blocks, response surface designs. Special emphasis on applications. 

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.  

Simple random sampling; stratified sampling; systematic sampling; multistage sampling; double sampling, ratio and regression estimates; sources of error in surveys. 

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 population and parameters. Distributions of functions and 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.  

Selected topics at an advanced level. Content will vary. Topic of course will be entered on student’s transcript. Course will be considered as an upper level elective for Information Sciences students and for Mathematics and Statistics Majors.

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.