Professors: J. C. Arnold; J. B. Birch; R. V. Foutz; D R. Jensen; J.P. Morgan; M. R. Reynolds, Jr.1; E. P. Smith; G.G. Vining; W.H. Woodall
Associate Professors: C. Anderson-Cook; G. I. Holtzman; A. M. McGuirk 2; R. S. Schulman; G. R. Terrell; K. Ye
Assistant Professors: S. Bates; D.J. Spitzner
Instructor: M. Marini
Lecturer: C.M. Box
1Joint with Forestry.
2Joint with Agricultural and Applied Economics.
Statistics courses are offered at both the undergraduate and the graduate levels for students preparing for professions in statistics, for students who need statistical tools to engage in scientific research, and for students who want to acquire knowledge of the important concepts of probability and statistical inference. The statistics department also has joined with the departments of computer science and mathematics to offer a course which provides an interdisciplinary approach to the study of mathematical sciences. (Please see "Mathematical Sciences" in this catalog.)
Statistics courses for graduate students and programs leading to the M.S. (with or without thesis) and Ph.D. degrees in statistics are described in the Graduate Catalog and in a special bulletin available from the department.
All statistics majors are required to own specified personal computers and software. Consult the department for details.
A special brochure describing the department and the B.S. program, intended for prospective entering freshmen, is available from the department upon request.
Students with sufficiently high GPA's may obtain permission to take graduate courses during the senior year. Graduate courses may serve as undergraduate free electives or as substitutes for required courses (e.g., 5104, 5114 for 4105, 4106). Under certain circumstances, students also may begin a graduate degree program during the senior year.
Cooperative Education positions are available in industry and government, offering valuable practical experience. The department encourages participation in the co-op program.
Requirements: 18 hours
The department reserves the right to withhold credit if a student takes a course, the content of which is partially duplicated in a course already taken (see Course Duplications below).
Associated with the statistics department, the consulting center provides statistical assistance for research projects throughout the university community. Faculty members, staff, and students are available to aid in statistical design and analysis for any authorized research study here at the university and at other state agencies.
University policy requires that students who are making satisfactory progress toward a degree meet minimum criteria toward the University Core (see "Academics" chapter in this catalog), toward the Arts and Sciences College Core (see first part of this chapter), and toward the degree in statistics. Satisfactory progress toward the B.S. in Statistics requires that:
| CS 1044 | 3 |
| MATH 1114, 1224, 1205, 1206, 2224 | 13 |
| STAT 3005, 3006: Statistical Methods | 6 |
| STAT 3104: Probability Theory and Stat. Inference | 3 |
| Credits | (25) |
|---|
Course Duplications: No credit will be given for more than one course in each of the following groups (in parentheses) of partially duplicated courses: (3005, 3615, 4604). (3006, 3616, 4706). (4105, 4604, 4705, 4714, 4724). No credit will be given for: 2004 if taken with or after any other statistics course; 2404 if taken with or after any of 3104, 4105, 4705, 4714, 4724; 3604 if taken with or after any statistics course except 2004, 2404, 3104. MSci 2405 may not be used as a substitute for credit as a statistics course. Exceptions to this rule may be granted if the student was officially registered as a Business major at the time MSci 2405 was taken. Exceptions to the duplications involving courses approved for the minor in statistics may be granted for students who are obtaining a minor in statistics, provided that the requirements listed under Minor in Statistics have been met.
Computer Literacy: Many statistics courses involve the use of statistics software; primarily MINITAB or SAS. Experience with the software is not expected, but students should have familiarity with either the Windows or Macintosh operating system and have access to a computer. These courses are identified by "WIN/MAC" under prerequisites.
Course Projects: Many of the upper-division course descriptions below include the word 'Project.' Those courses will usually include a major term project, either individually or in small groups. These projects are designed to give students the kind of insight and experience in realistic statistical practice that cannot be obtained in classroom lectures or short-term homework assignments.
2004: INTRODUCTORY STATISTICS
Fundamental concepts and methods of statistics with emphasis on interpretation of statistical arguments. An introduction to design of experiments, data analysis, correlation and regression, concepts of probability theory, sampling errors, confidence intervals, and hypothesis tests. (See also Course Duplications). Pre: MATH 1015. (3H,3C) I,II,III.
2404: INTRODUCTION TO PROBABILITY
Axiomatic, frequency, and personal approaches to probability; basic combinatorics and their applications to games of chance; random variables and their distributions; probabilistic meaning of statistical tests; Bayesian concepts and odds ratios. See also Course Duplications, above. Even years. Pre: MATH 1015. (3H,3C) I.
2954:INTRODUCTION TO DATA MANAGEMENT AND SAS
Introduction to computer workstations, UNIX command language, common desktop environment. Computer networking concepts. Data management and presentation in the Statistical Analysis System (SAS), including data input, manipulation, graphs, macros. Co: 3005. (1H,2L,2C)
2964: FIELD STUDY
Pass/Fail only. Variable credit course.
3005-3006: STATISTICAL METHODS
3005: Basic statistical methodology: exploratory data techniques, estimation, inference, comparative analysis by parametric, nonparametric, and robust procedures. Analysis of variance (one-way), multiple comparisons, and categorical data. 3006: Analysis of variance, simple and multiple, linear and nonlinear regression, analysis of covariance. Use of MINITAB. WIN/MAC. Pre: MATH 1206. (3H,3C) 3005: I,II,III; 3006: I,II,IV.
3104: PROBABILITY AND DISTRIBUTIONS
Probability theory, including set theoretic and combinatorial concepts; in-depth treatment of discrete random variables and distributions, with some introduction to continuous random variables; introduction to estimation and hypothesis testing. Pre: MATH 1206 or MATH 2015 or MATH 1526. (3H,3C) I,II.
3504: NONPARAMETRIC STATISTICS
Statistical methodology based on ranks, empirical distributions, and runs. One and two sample tests, ANOVA, correlation, goodness of fit, and rank regression, R-estimates and confidence intervals. Comparisons with classical parametric methods. Emphasis on assumptions and interpretation. WIN/MAC, even years. Pre: 3006, 4106, 4604, 4706. (3H,3C) I.
3604: STATISTICS FOR THE SOCIAL SCIENCES
Statistical methods for nominal, ordinal, and interval levels of measurement. Topics include descriptive statistics, elements of probability, discrete and continuous distributions, one and two sample tests, measures of association. Emphasis on comparison of methods and interpretations at different measurement levels. (See also Course Duplications). Pre: MATH 1015. (3H,3C) I,II,IV.
3615-3616: BIOLOGICAL STATISTICS
Descriptive and inferential statistics in a biological context. 3615: Fundamental principles, one- and two-sample parametric inference, simple linear regression, frequency data. 3616: One- and two-way ANOVA, multiple regression, correlation, nonparametrics, using the MINITAB computer package. (3H,3C) 3615: I,II,III; 3616: II,IV.
3704: STATISTICS FOR ENGINEERING APPLICATIONS
Introduction to statistical methodology with emphasis on engineering experimentation: probability distributions, estimation, hypothesis testing, regression, and analysis of variance. Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit. Pre: MATH 2224. (2H,2C) I,II.
4004: METHODS OF STATISTICAL COMPUTING
Computationally intensive computer methods used in statistical analyses. Statistical univariate and multivariate graphics; resampling methods including bootstrap estimation and hypothesis testing and simulations; classification and regression tress; scatterplot smoothing and splines. (4H,3C)
4105-4106: THEORETICAL STATISTICS
4105: Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics. 4106: Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance. Pre: MATH 2224. (3H,3C) 4105: I; 4106: II.
4204: EXPERIMENTAL DESIGNS
Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Discussion of completely randomized, randomized complete block, and latin square designs, analysis of covariance, splitplot designs, factorial and fractional designs, incomplete block designs. Project. Knowledge of WIN/MAC required. Pre: 3006 or 3616 or 4106 or 4706 or 5605 or 5615. (3H,3C) I.
4214: METHODS OF REGRESSION ANALYSIS
Multiple regression including variable selection procedures; detection and effects of multicollinearity; identification and effects of influential observations; residual analysis; use of transformations. Non-linear regression, the use of indicator variables, and logistic regression. Use of SAS. Project. Knowledge of WIN/MAC required. Pre: 3006 or 3616 or 4106 or 4706 or 5606 or 5616. (3H,3C) I.
4444: APPLIED BAYESIAN STATISTICS
Introduction to Bayesian methodology with emphasis on applied statistical problems: data displaying, prior distribution elicitation, posterior analysis, models for proportions, means and regression. Pre: MATH 2224. (3H,3C)
4504: APPLIED MULTIVARIATE ANALYSIS
Non-mathematical study of multivariate analysis. Multivariate analogs of univariate test and estimation procedures. Simultaneous inference procedures. Multivariate analysis of variance, repeated measures, inference for dispersion and association parameters, principal components analysis, discriminant analysis, cluster analysis. Use of SAS. Project. Knowledge of WIN/MAC required, even years. Pre: 3006 or 4706 or 5606 or 5616. (3H,3C) II.
4514: CONTINGENCY TABLE ANALYSIS
Statistical techniques for frequency data. Goodness-of-fit. Tests and measures of association for two-way tables. Log-linear models for multidimensional tables. Parameter estimation, model selection, incomplete tables, ordinal categories, logistic regression. Use of BMDP and SPSSx. Project. Knowledge of WIN/MAC required, even years. Pre: 3006 or 3616 or 4106 or 4706 or 5606 or 5616. (3H,3C) II.
4524: SAMPLE SURVEY METHODS
Statistical methods for the design and analysis of survey sampling. Fundamental survey designs. Methods of randomization specific to various survey designs. Estimation of population means, proportions, totals, variances, and mean squared errors. Design of questionnaires and organization of a survey. Project. Odd years. Pre: 3006 or 3616 or 4106 or 4706 or 5606 or 5616. (3H,3C) I.
4534: APPLIED STATISTICAL TIME SERIES ANALYSIS
An applied course in time series analysis. A uniform coverage of both time domain and frequency domain methods that are used in the physical, biological, and social sciences and by applied statisticians. WIN/MAC. Odd years. Pre: MATH 1206, 3006 or 4106 or 4706 or 4714 or 5606 or 5616. (3H,3C) II.
4604: STATISTICAL METHODS FOR ENGINEERS
Introduction to statistical methodology with emphasis on engineering applications: probability distributions, estimation, hypothesis testing, regression, analysis of variance, quality control. Only one of the courses 4604, 4705, and 4714 may be taken for credit. Knowledge of WIN required. Pre: MATH 1206. (3H,3C) I,II.
4705-4706: PROBABILITY AND STATISTICS FOR ENGINEERS
Basic concepts of probability and statistics with emphasis on engineering applications. 4705: Probability, random variables, sampling distributions, estimation, hypothesis testing, simple linear regression correlation, one-way analysis of variance. 4706: Multiple regression, analysis of variance, factorial and fractional experiments. Only one of the courses 4604, 4705, and 4714 may be taken for credit. Knowledge of WIN/MAC required. Pre: MATH 2224 for 4705; 4705 for 4706. (3H,3C) 4705: I,II,III; 4706: I,II.
4714: PROBABILITY AND STATISTICS FOR ELECTRICAL ENGINEERS
Introduction to the concepts of probability, random variables, estimation, hypothesis testing, regression, and analysis of variance with emphasis on application in electrical engineering. Only one of the courses 4604, 4705, and 4714 may be taken for credit. Pre: MATH 2224. (3H,3C) I,II,III.
4724: STATISTICAL THEORY FOR ECONOMISTS
Probability, random variables, marginal and conditional distributions, mathematical expectations, sampling distributions, properties of estimators, maximum likelihood and least squares estimation, confidence intervals, hypothesis tests, linear regression. Emphasis on preparation for graduate study in econometrics. Pre: MATH 2015, 3006. (3H,3C) I.
4804 (AAEC 4804): ELEMENTARY ECONOMETRICS
Economic applications of mathematical and statistical techniques: regression, estimators, hypothesis testing, lagged variables, discrete variables, violations of assumptions, simultaneous equations. Pre: AAEC 1006, 3005 or 3604. (3H,3C) II.
4954: PROFESSIONAL PORTFOLIO
Preparation of a portfolio of professional quality statistical reports, using the student's term reports from three upper division applied statistics courses. Each student will choose a faculty mentor to work with in preparation of the portfolio. Statistics major with Senior standing. Pass/Fail only. (1H,1C) I, II, III, IV.
4964: FIELD STUDY
Pass/Fail only. Variable credit course.
4974: INDEPENDENT STUDY
Variable credit course.
4984: SPECIAL STUDY
Variable credit course.
4994: UNDERGRADUATE RESEARCH
Variable credit course.
Please see the Graduate Catalog for graduate course listings.
Virginia Tech -- Undergraduate Catalog, 2002-2004
Last update: August 2002
URL: http://www.vt.edu/academics/ugcat/ucdStats.html