Undergraduate Program Courses

1301. Introduction to Statistics. Introduction to collecting observations and measurements, organizing data, variability, and fundamental concepts and principles of decision-making. Emphasis is placed on statistical reasoning and the uses and misuses of statistics.

2301. Statistics for Modern Business Decisions. A foundation in data analysis and probability models is followed by elementary applications of condence intervals, hypothesis testing, correlation, and regression. Prerequisite: CEE Math Fundamentals or equivalent.

2331. Introduction to Statistical Methods. An introduction to statistics for behavioral, biological, and social scientists. Topics include descriptive statistics, probability and inferential statistics including hypothesis testing, and contingency tables.

3312. Categorical Data Analysis. Examines techniques for analyzing data that are described by categories or classes. Discusses classical chi-square tests and modern log-linear models. Emphasizes practical applications using computer calculations and graphics. Prerequisite: STAT 2301 or 2331, or equivalent.

3380. Environmental Statistics. Examines statistical design and analysis methods relevant to environmental sampling, monitoring, and impact assessment. Emphasizes statistical procedures that accommodate the likely temporal and spatial correlation in environmental data. Prerequisite: STAT 2301 or 2331, or equivalent.

3385. Introduction to Nonparametric Statistics. Statistical methods that do not require explicit distributional assumptions such as normality. Analyses based on ranks. One- and multi-sample procedures. Tests of randomness and independence. Prerequisite: STAT 2301 or 2331, or equivalent.

4340 (EMIS 4340). Statistical Methods for Engineers and Applied Scientists. Basic concepts of probability and statistics useful in the solution of engineering and applied science problems. Topics: probability, probability distributions, data analysis, sampling distributions, estimation, and simple tests of hypothesis. Prerequisites: MATH 1337 and 1338.

For Undergraduate and Graduate Students

These courses do not carry graduate credit for students in the Ph.D. program in statistical science.   They do carry graduate credit for students in the MASDA program.

5304.  Introduction to Statistical Computing.  This course introduces students to statistical computing with an emphasis on SAS programming.  Students will learn how to read, write, and import data; prepare data for analysis; use SAS procedures; and create graphs.

5340 (EMIS 5370). Probability and Statistics for Scientists and Engineers. Introduction to fundamentals of probability and distribution theory, statistical techniques used by engineers and physical scientists. Examples of tests of signicance, operating characteristic curve, tests of hypothesis about one and two parameters, estimation, analysis of variance, and the choice of a particular experimental procedure and sample size. Prerequisites: MATH 1337, 1338, and 2339, or equivalent.

5344 (EMIS 5364). Statistical Quality Control. Statistics and simple probability are introduced in terms of problems that arise in manufacturing; their application to control of manufacturing processes. Acceptance sampling in terms of standard sampling plans: MIL-STD 105, MIL-STD 414, Dodge-Romig plans, continuous sampling plans, etc. Prerequisite: STAT (EMIS) 4340 or STAT 5340 (EMIS 5370).

5370. Survey Sampling. Principles of Planning and Conducting Surveys. Simple random sampling; stratified, systematic, subsampling; means, variances, confidence limits; finite population correction; sampling from binomial populations; margin of error and sample-size determination. Prerequisite: STAT 2301 or 2331, or equivalent.  Co-requisite of STAT 5304.  (STAT 3370 prior to 2013-14.) 

5371. Experimental Statistics. A non-calculus development of the fundamental procedures of applied experimental statistics, including tests of hypotheses and interval estimation for the normal, binomial, chi-square and other distributions, and nonparametric tests. Prerequisite: Junior standing or permission of instructor.  (Co-requisite of STAT 5304).

5372. Experimental Statistics. Analysis of variance, completely randomized design, randomized complete block designs-nested classications, factorials; analysis of covariance, simple and multiple linear regressions, and correlation. Prerequisite: STAT 5371.

5373. Introduction to Mathematical Statistics.  Topics include: probability; probability distributions; mathematical expectation; discrete and continuous random variables and their distributions; sampling distributions; moment generating function; functions of random variables; confidence intervals. Prerequisite: MATH 2339

5374: Mathematical Statistics II: Second course in Mathematical Statistics.  Topics include: order statistics; limiting distributions; central limit theorem; point estimation; testing statistical hypotheses; Bayesian procedures; nonparametric methods. Prerequisite: STAT 5373

5377. Statistical Design and Analysis of Experiments. Introduction to statistical principles in the design and analysis of industrial experiments. Completely randomized, randomized complete and incomplete block, Latin square, and Plackett-Burman screening designs. Complete and fractional factorial experiments. Descriptive and inferential statistics. Analysis of variance models. Mean comparisons. Prerequisite: STAT 4340 or 5371, or permission of instructor.