Professor Richard Gunst, Department Chair
Professors: Henry Gray, William Schucany, Lynne Stokes, Wayne Woodward; Associate Professor: Ian Harris; Assistant Professors: Jing Cao, Monnie McGee, Hon Keung Ng, Sherry Wang; Emeritus Professors: Narayan Bhat, 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. Two tracks are available for students interested in a statistical science major. The Bachelor of Science in Applied Statistics track prepares students for careers in industry, government, or business by emphasizing the development of students' skills in data analysis and the proper interpretation of data. It is intended to be a terminal degree track and should not be the preferred track for students intending to pursue advanced studies in statistical science. The Bachelor of Science in Statistical Science track prepares students for advanced studies in statistical science, such as graduate work in the field or in a related discipline. Since statistics is a science based on correct mathematical formation and careful adherence to underlying theoretical principles, this track places more emphasis on mathematics preparation than does the other track.
STAT 5371, 5372, 4399
Electives 27 hours selected from the following, including at least 12 advanced hours in STAT
STAT 1301 or 2301 or 2331 or ITOM 2305 (no more than one), 3300, 3312, 3341, 3370, 3380, 4340, 4385, 5377
EMIS 3360, 5369, 7361
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), 3300, 3312, 3341, 3370, 3380, 4385, 5377
MATH 2343, 3353 (highly recommended) or other advanced courses
EMIS 3360, 5369, 7361
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, including the specified hours in each of the following three categories.
STAT 1301, 2301 or 2331 or ITOM 2305 (no more than one); ITOM 2308; EMIS 3360, 5369, 7361; ECON 5350 (at least 3 hours)
STAT 3300, 3312, 3341, 3370, 3380, 4385, 5377; PSYC 3382 (at least 6 hours)
STAT 5371, 5372 (6 hours)
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.
3300. Applied Statistical Data Analysis. Emphasizes the analysis of data using state-of-the-art statistical methods and specialized statistical software. Case studies form a major component of the course requirements.
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.
3341. Statistical Design and Analysis of Experiments. Fundamental principles and procedures for the statistical design of industrial and scientific experiments form the core of this course. Complete and fractional factorial experiments in completely randomized, randomized block, and nested designs are covered. The statistical analysis of these experiments, using appropriate statistical software, also will be emphasized.
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.
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 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).
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.
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.
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.