Concepts of population and sample; data collection strategies; frequency distributions; descriptive statistics and elementary data visualizations; contingency tables and the basic concepts of theoretical and empirical probability; conditional probabilities; random variables, distribution functions, expectations, and variances; the Normal distribution; sampling distributions and the Central Limit Theorem; statistical inference for one and two population means; one-way analysis of variance; correlation and regression estimation. Note: Students counting credit for STAT 1793 cannot count credit for BA 1605, PSYC 2901 and/or STAT 2593.

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: Students counting credit for STAT 2593 cannot count credit for BA 1605, PSYC 2901, and/or STAT 1793.

Special discrete and continuous probability distributions; concepts of estimation; sampling distributions, the Central Limit Theorem, and its applications; confidence interval estimation and parametric hypothesis tests for proportion(s), mean(s) and standard deviation(s); the analysis of paired data; nonparametric hypothesis testing; simple linear regression, with prediction and inference; the analysis of variance. Further concepts in statistics and probability, as time permits. Note: Students counting credit for STAT 2793 cannot count credit for BA 2606, and/or PSYC 3913. 

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

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.

Topics 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 cannot be counted for both STAT 4703, and ECON 4645. 

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.

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 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.