Dedman College
(2010 Undergraduate Catalog)
Statistical Science
www.smu.edu/statistics
Professor Wayne Woodward,
Department Chair
Professors: Ronald Butler, Richard Gunst, William Schucany, Lynne Stokes, Wayne Woodward.
Associate Professors: Ian Harris, Monnie McGee, Hon Keung Ng, Sherry Wang.
Assistant Professor: Jing Cao.
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 B.S. in statistical science prepares students for advanced studies in statistical science, such as graduate work in the field or in a related discipline.
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MATH 1337, 1338, 2339
STAT 4340 or 5340, 5371, 5372, 4399
Electives – 21 hours selected from the following, including at least 9 hours in advanced statistics
STAT 1301 or
2301 or
2331 or
ITOM 2305 (no more than one),
3312, 3370, 3380, 4385, 5377
MATH 2343, 3353 (highly recommended)
EMIS 3360, 5361, 5369
ECON 5350, 5375, 5385
or other approved courses
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 2301 (at least 6 hours)
STAT 5371, 5372 (6 hours)
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1301. Introduction to Statistics. Introduction to collecting observations and measurements, organizing data, accounting for variability, and applying 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: GEC 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.
Prerequisite: GEC Math Fundamentals or equivalent.
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; and 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 (CSE 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 include probability, probability distributions, data analysis, sampling distributions, estimation and simple tests of hypothesis.
Prerequisites: MATH 1337 and 1338.
4385. Introduction to Nonparametric Statistics. A survey of statistical methods that do not require explicit distributional assumptions such as normality. One- and multisample analyses based on ranks and permutation procedures. Introduction to bootstrapping, simulation and nonparametric regression.
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, including 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.
5110 and 5310. Independent Study in Statistical Science. Independent study of a selected topic in statistical science. Individual study under direction of a faculty member allowed for 5110; group projects allowed for 5310.
5340 (EMIS 5370). Probability and Statistics for Scientists and Engineers. Introduction to fundamentals of probability and distribution theory, and 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, as well as their application to control of manufacturing processes. Includes acceptance sampling in terms of standard sampling plans: MIL-STD 105, MIL-STD 414, Dodge-Romig plans, continuous sampling plans, etc.
Prerequisite: STAT 4340 or 5340.
5371. Experimental Statistics I. A noncalculus development of the fundamental procedures of applied experimental statistics, beginning with tests of hypotheses and interval estimation for the normal and binomialdistributions, and introducing power analysis and sample-size estimation for a variety of multiple-treatment designs.
Prerequisite: STAT 2301 or 2331, or permission of instructor.
5372. Experimental Statistics II. A survey of multivariate statistical methods in an applied setting, including multiple regression, dichotomous and polytomous logistic regression, multivariate analysis of variance, linear and quadratic discriminate analysis, and factor analysis and principal components analysis.
Prerequisite: STAT 5371.
5377. Statistical Design and Analysis of Experiments. Introduction to statistical principles in the design and analysis of industrial experiments. Covers completely randomized, randomized complete and incomplete block, Latin square, and Plackett-Burman screening designs. Includes complete and fractional factorial experiments, descriptive and inferential statistics, analysis of variance models, and mean comparisons.
Prerequisite: STAT 4340 or 5371, or permission of instructor.
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