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, 415758, and either 2183 or 4197, plus three approved upperdivision 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 computerintensive 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 problemsolving using Pascal. Handson 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, chisquare, 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 ksample 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 nonsampling errors. Prerequisite: Stat 1051 or equivalent. 
415758 
Introduction to Mathematical Statistics (33) 
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 415758 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) 
418990 
Mathematical Probability and Applications (33) 
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 (cointossing 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. 
