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University Bulletin: Undergraduate Programs The George Washington University  

 
   
 

STATISTICS

Professors J.L. Gastwirth, J.M. Lachin III, H.M. Mahmoud, T.K. Nayak, Z. Li, J. Chandra (Research), R. Modarres (Chair)

Associate Professors S. Bose, E. Bura, S. Kundu, M. Larsen, Y. Lai, J.R. Stroud

Assistant Professors S. Balaji, Q. Pan, J. Landon, T. Apanasovich

Professorial Lecturers F. Ponti, P. Chandhok, C.M. Fleming

Bachelor of Science with a major in statistics—The following requirements must be fulfilled:

1. The general requirements stated under Columbian College of Arts and Sciences.

2. Prerequisite courses—Math 1231, 1232, 2233; an introductory course in statistical methods.

3. Required courses in the major—Math 2184; Stat 2118, 3119, 1129, 4157-58, and either 2183 or 4197, plus three approved upper-division courses, some of which, in special circumstances, may be taken in other departments. To assure a balanced program, departmental approval of electives is required for all majors.

Students who seek Special Honors in statistics should check with the Department.

Minor in statistics—18 hours of approved courses in this department, including an introductory statistics course, Stat 2118 or 2123, and one computer-intensive course.

With permission, a limited number of graduate courses in the department may be taken for credit toward an undergraduate degree. See the Graduate Programs Bulletin for course listings.

Note: Stat 1051, 1053, 1111, and 1127 are related in their subject matter, and credit for only one of these courses may be applied toward a degree. One entrance unit in algebra is prerequisite to all courses in statistics.

1051 Introduction to Business and Economic Statistics (3) Nayak and Staff
  Lecture (3 hours), laboratory (1 hour). Frequency distributions, descriptive measures, probability, probability distributions, sampling, estimation, tests of hypotheses, regression and correlation, with applications to business. (Fall and spring)
1053 Introduction to Statistics in Social Science (3) Balaji and Staff
  Lecture (3 hours), laboratory (1 hour). Frequency distributions, descriptive measures, probability, sampling, estimation, tests of hypotheses, regression and correlation, with applications to social sciences. (Fall and spring)
1111 Business and Economic Statistics I (3) Gastwirth, Bura
  Descriptive statistics, graphical methods, probability, special distributions, random variables, sampling, estimation and confidence intervals, hypothesis testing, correlation and regression. (Fall)
1127 Statistics for the Biological Sciences (3) Lai
  Introduction to statistical techniques and reasoning applicable to the biomedical and related sciences. Properties of basic probability functions: binomial, Poisson, and normal. Data analysis, inference, and experimental design. (Spring)
1129 Introduction to Computing (3) Teitel
  Introduction to elements of computer programming and problem-solving using Pascal. Hands-on experience will be acquired through computer programming projects, including some simple statistical applications. (Fall and spring)
2105 Statistics in the Behavioral Sciences (3) Staff
  Lecture (3 hours), laboratory (1 hour). Advanced study of statistical techniques for research problems. Analysis of variance, correlation techniques, nonparametric techniques, sampling theory. Prerequisite: an introductory statistics course and satisfactory performance on a placement examination. (Fall)
2112 Business and Economic Statistics II (3) Gastwirth, Bura
  Continuation of Stat 1111, with emphasis on techniques of regression, chi-square, nonparametric inference, index numbers, time series, decision analysis, and other topics used in economics and business. Prerequisite: Stat 1111 or equivalent. (Fall and spring)
2118 Regression Analysis (3) Kundu
  Lecture (3 hours), laboratory (1 hour). Simple and multiple linear regression, partial correlation, residual analysis, stepwise model building, multicollinearity and diagnostic methods, indicator variables. Prerequisite: an introductory statistics course. (Fall and spring)
2123 Introduction to Econometrics (3) Staff
  Same as Econ 2123.
2183 Intermediate Statistical Laboratory: Statistical Computing Packages (3) Landon, Modarres
  Application of program packages (e.g., SAS, SPSS) to the solution of one-, two- and k-sample parametric and nonparametric statistical problems. Basic concepts in data preparation, modification, analysis and interpretation of results. Prerequisite: an introductory statistics course.
3119 Analysis of Variance (3) Staff
  Lecture (3 hours), laboratory (1 hour). Introduction to the design of experiments and analysis of variance; randomized block, factorial, Latin square designs, and analysis of covariance. Prerequisite: Stat 2118. (Spring)
3187 Introduction to Sampling (3) Nayak
  Problems of sampling and sample design. Simple random, stratified, systematic, cluster, and multistate designs; control of sampling and non-sampling errors. Prerequisite: Stat 1051 or equivalent.
4157-58 Introduction to Mathematical Statistics (3-3) Pan, Mahmoud
  Stat 4157: Basic concepts of probability theory, including random variables, independence, distribution theory, and sampling theory. Stat 4158: Inference procedures, including estimation, hypothesis testing, regression analysis, and experimental design. Prerequisite: Math 1232 or equivalent. (Academic year)
4181 Applied Time Series Analysis (3) Stroud
  Autoregressive integrated moving average (ARIMA) modeling and forecasting of univariate time series. Estimation of spectral density functions, white noise tests, and tests for periodicities. Theory and applications using SAS. Prerequisite: Math 2233, Stat 4157-58 or 2118. (Spring)
4188 Nonparametric Statistical Inference (3) Staff
  Statistical inference when the form of the underlying distribution is not fully specified. Nonparametric procedures for estimation and testing hypotheses. An introduction to robust procedures. Prerequisite: Stat 1051 or equivalent. (Fall, even years)
4189-90 Mathematical Probability and Applications (3-3) Mahmoud
  Probability theory, including combinatorial analysis, conditional probability, and stochastic independence. Random variables and their distributions; laws of large numbers and central limit theorem. Application of concepts to elementary stochastic processes (coin-tossing sequences, branching processes, Markov chains). Prerequisite: Math 1232 or equivalent. (Alternate academic years)
4195 Reading and Research (arr.) Staff
  May be repeated once for credit. Admission by permission of department chair. (Fall and spring)
4197 Fundamentals of SAS Programming for Data Management (3) Landon, Modarres
  Fundamentals of the SAS system for data management, statistical analysis, and report writing. Data modification; programming; file handling; and macro writing. Prerequisite: An introductory statistics course and Stat 1129.  (Spring)
4198 Special Topics (3) Staff
  Topic to be announced in the Schedule of Classes. May be repeated for credit provided the content differs.
 

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© 2013 University Bulletin
The George Washington University All rights reserved.

Information in this bulletin is generally accurate as of fall 2012. The University reserves the right to change courses, programs, fees, and the academic calendar, or to make other changes deemed necessary or desirable, giving advance notice of change when possible.