**100(300). Introduction to Statistical Reasoning. *** No
credit granted to those who have completed or are enrolled in
Soc. 210, Poli.Sci. 280, Stat. 402, 311, 405, or 412, or Econ.
404. (4). (NS). *

This course is designed to provide an overview of the field statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. Alternative approaches to statistical inference are also discussed. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Evaluation is based upon three hourly in-class examinations and a final examination. The course format is lecture with some discussion. Cost:2 WL:3

**402. Introduction to Statistics and Data Analysis.
*** No credit granted to those who have completed or
are enrolled in Econ. 404 or Stat. 311, 405, or 412. (4). (NS). *

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 402 has no prerequisite and has been elected by students whose mathematics background includes only high school algebra. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods. The course format includes three lectures and a laboratory (l.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a micro-computer package and the Macintosh computer. Course evaluation is based on a combination of three examinations GIVEN WEDNESDAY EVENINGS, a final examination and teaching fellow input. [Cost: 2] [WL: 3]

**403. Introduction to Statistics and Data Analysis II.
*** Stat. 402. (4). (Excl). *

APPLIED REGRESSION. The course will also cover various topics associated with the general linear model emphasizing applications. Topics include: multiple regression, variable selection, stepwise regression, residual analysis, analysis of variance models, covariance analysis and principal components. OTHER TOPICS. As time allows, the course may cover some aspects of probit and logic analyses, analysis of time series data, reliability analysis, and topics in experimental design. Three hours of lecture and one and one-half hours of lab per week. Cost:2 WL:3

**405/Econ. 405. Introduction to Statistics. *** Math.
115 or permission of instructor. Juniors and seniors may elect
concurrently with Econ. 201 and 202. No credit granted to those
who have completed or are enrolled in Econ. 404 or Stat. 311 or
412. (4). (Excl). *

See Economics 405. (Kmenta)

**412. Introduction to Probability and Statistics. *** Prior
or concurrent enrollment in Math. 215 and CS 183. No credit granted
to those who have completed or are enrolled in 311 or 405. One
credit granted to those who have completed Stat. 402. (3). (Excl). *

The objectives of this course are to introduce students to the basic ideas of probability and statistical inference and to acquaint students with some important data analytic techniques, such as regression and the analysis of variance. Examples will emphasize applications to the natural sciences and engineering. There will be regular homework, including assignments which require the use of MTS, two midterms, and a final exam. Cost:3 WL:3

**425/Math. 425. Introduction to Probability. *** Math.
215. (3). (N.Excl). *

See Mathematics 425.

**426. Introduction to Mathematical Statistics. *** Stat.
425. (3). (NS). *

This course covers the basic ideas of statistical inference, including sampling distributions, estimation, confidence intervals, hypothesis testing, regression, analysis of variance, nonparametric testing, and Bayesian inference. The sequence of Statistics 425/426 serves as a prerequisite for more advanced Statistics courses. Weekly problem sets, two hourly exams, and one final exam. WL:3

**504. Seminar on Statistical Consulting. *** Stat.
403 or 500. (1-4). (Excl). May be repeated for a total of 8 credits. *

Applications of statistics to problems in the sciences; students will be expected to analyze data and write reports.

**506. Statistical Computing. *** Stat. 426
or 500, and CS 380 or 283, or permission of instructor. (3). (Excl). *

Selected topics in statistical computing, including: Monte Carlo procedures, generation of random numbers, computation of estimators, linear and non-linear problems, resampling algorithms, splines, other special topics.

**525(510)/Math. 525. Probability Theory. *** Math.
450 or 451; or permission of instructor. Students with credit
for Math. 425/Stat. 425 can elect Math. 525/Stat. 525 for only
1 credit. (3). (Excl). *

See Mathematics 525.

**550/SMS 576/I.O.E. 560. Bayesian Decision Analysis.
*** Stat. 425 or permission of instructor. (3). (Excl). *

Axiomatic foundations for personal probability and utility; interpretation and assessment of personal probability and utility; formulation of Bayesian decision problems; risk functions, admissibility likelihood principle and properties of likelihood functions; natural conjugate prior distributions; improper and finitely additive prior distributions; examples of posterior distributions, including the general regression model and contingency tables; Bayesian credible intervals and hypothesis tests; application to a variety of decision-making situations. There will be assigned homework exercises, a midterm and a final exam. WL:3

**570. Experimental Design. *** Stat. 426 and a basic knowledge of matrix algebra; or permission of instructor.
(3). (Excl). *

Basic topics and ideas in the design of experiments: randomization tests; the validity and analysis of randomized experiments; randomized blocks; latin and Graeco-Latin squares; plot techniques; factorial experiments; the use of confounding and response surface methodology; weighing designs, lattice and incomplete block and partially balanced incomplete block designs.

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