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STATISTICS
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Professors J.L. Gastwirth, N.D. Singpurwalla, J.M. Lachin III, H.M. Mahmoud, T.K. Nayak, Z. Li, J. Chandra (Research), R. Modarres (Chair)
Associate Professors S. Bose, E. Bura
Assistant Professors S. Kundu, S. Balaji, Y. Lai, Q. Pan, J.R. Stroud
Professorial Lecturers F. Ponti, P. Chandhok, R.F. Teitel, C.M. Fleming
Lecturer H. Modarres
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 31, 32, 33; an introductory course in statistical methods.
3. Required courses in the major—Math 84; Stat 118, 119, 129, 157—58, and either 183 or 197, plus three approved 100-level 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 118 or 123, 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 51, 53, 104, 111, and 127 are related in their subject matter, and credit for only one of the five may be applied toward a degree. One entrance unit in algebra is prerequisite to all courses in statistics.
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| 51 |
Introduction to Business and Economic Statistics (3) |
Nayak and Staff |
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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) |
| 53 |
Introduction to Statistics in Social Science (3) |
Balaji and Staff |
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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) |
| 104 |
Statistics in Management, Administration, and Policy Studies (3) |
Staff |
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Lecture (3 hours), laboratory (1 hour). Introductory study of statistical techniques for research problems. For graduate students in fields other than statistics who have no previous statistics training. Offered off campus only. |
| 105 |
Statistics in the Behavioral Sciences (3) |
Staff |
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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) |
| 111 |
Business and Economic Statistics I (3) |
Gastwirth, Bura |
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Descriptive statistics, graphical methods, probability, special distributions, random variables, sampling, estimation and confidence intervals, hypothesis testing, correlation and regression. (Fall) |
| 112 |
Business and Economic Statistics II (3) |
Gastwirth, Bura |
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Continuation of Stat 111, 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 111 or equivalent. (Fall and spring) |
| 118 |
Regression Analysis (3) |
Kundu |
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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) |
| 119 |
Analysis of Variance (3) |
Staff |
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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 118. (Spring) |
| 123 |
Introduction to Econometrics (3) |
Staff |
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Same as Econ 123. |
| 127 |
Statistics for the Biological Sciences (3) |
Lai |
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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) |
| 129 |
Introduction to Computing (3) |
Mahmoud, Teitel |
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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) |
| 157—58 |
Introduction to Mathematical Statistics (3—3) |
Pan, Mahmoud |
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Stat 157: Basic concepts of probability theory, including random variables, independence, distribution theory, and sampling theory. Stat 158: Inference procedures, including estimation, hypothesis testing, regression analysis, and experimental design. Prerequisite: Math 32 or equivalent. (Academic year) |
| 173 |
Discrete Systems Simulation (3) |
Staff |
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Same as EMSE 173. |
| 181 |
Applied Time Series Analysis (3) |
Stroud |
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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 33, Stat 157—58 or 118. (Spring) |
| 183 |
Intermediate Statistical Laboratory:Statistical Computing Packages (3) |
Modarres |
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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. (Fall and spring) |
| 187 |
Introduction to Sampling (3) |
Nayak |
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Problems of sampling and sample design. Simple random, stratified, systematic, cluster, and multistate designs; control of sampling and non-sampling errors. Prerequisite: Stat 91 or equivalent. (Fall) |
| 188 |
Nonparametric Statistical Inference (3) |
Staff |
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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 91 or equivalent. (Fall, even years) |
| 189—90 |
Mathematical Probability and Applications (3—3) |
Mahmoud |
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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 32 or equivalent. (Alternate academic years) |
| 195 |
Reading and Research (arr.) |
Staff |
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May be repeated once for credit. Admission by permission of department chair. (Fall and spring) |
| 197 |
Fundamentals of SAS Programming for Data Management (3) |
Modarres, Teitel |
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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 129. (Spring) |
| 198 |
Special Topics (3) |
Staff |
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Topic to be announced in the Schedule of Classes. May be repeated for credit provided the content differs. |
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