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DEDMAN COLLEGE
STATISTICAL SCIENCE
Professor Wayne Woodward, Department Chair
Professors: Ronald Butler, Richard Gunst, William Schucany,
Lynne Stokes; Associate Professor: Ian Harris; Assistant
Professors: Jing Cao, Monnie McGee, Hon Keung Ng, Sherry Wang; Emeritus
Professors: Narayan Bhat, Henry Gray, Chandrakant Kapadia, Campbell
Read.
Statistics is the science of collecting, analyzing and interpreting data. The science of statistics is applicable in every setting where decisions are to be made or knowledge is to be advanced based on the analysis of data. Application fields include almost every academic discipline, including business, engineering and the natural and social sciences. Selecting the best medical treatment for a particular form of cancer, determining whether to use sampling methods to augment a census, and evaluating temperature trends for evidence of greenhouse-induced climate change are diverse examples of settings in which statistical science has made important contributions. Because of its interdisciplinary nature, statistical science is an exciting and valuable double major or minor.
Requirements for the B.S. Degree. The Bachelor of Science in Statistical Science prepares students for advanced studies in statistical science, such as graduate work in the field or in a related discipline.
Requirements for the Bachelor of Science in Statistical Sciences (42 hours)
MATH 1337, 1338, 2339
STAT 4340 or 5340, 5371, 5372, 4399
Electives – 21 hours selected from the following, including at least 9 advanced hours in STAT
STAT 1301 or 2301 or 2331 or ITOM 2305 (no more than one), 3312, 3370, 3380, 4385, 5377
MATH 2343, 3353 (highly recommended) or other advanced courses
EMIS 3360, 5361, 5369
ECON 5350, 5375, 5385
Requirements for the Minor. A minor in statistical science is a valuable complement to majors in the natural or social sciences, engineering, or business. Students planning careers that involve the collection, processing, description and/or the analysis of quantitative information will enhance their career opportunities with a minor in statistical science. A minor in statistical science requires at least 15 term hours, as specified below.
STAT 1301, 2301 or 2331 or ITOM 2305 (no more than 3 hours).
STAT 3312, 3370, 3380, 4385, 5377; PSYC 3382 (at least 6 hours)
STAT 5371, 5372 (6 hours)
The Courses (STAT)
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 confidence 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.
3370. 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.
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.
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.
4385. 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.
4399. Statistical Science in Practice. Practical experience on projects dealing with the collection, analysis and interpretation of data. Three to four major projects, one of the student’s design. Case studies from a variety of disciplines. Prerequisite: Statistical Science major or minor with senior class standing.
For Undergraduate and Graduate Students
These courses do not carry graduate credit for students in the M.S. program or in the Ph.D. program in statistical science.
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 significance, 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).
5371. Experimental Statistics I. 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.
5372. Experimental Statistics II. Analysis of variance, completely randomized design, randomized complete block designs-nested classifications, factorials; analysis of covariance, simple and multiple linear regressions, and correlation. Prerequisite: STAT 5371.
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.
