Take me to the Fall Time Schedule Courses in Statistics (Division 489)
100. Introduction to
Statistical Reasoning. No credit granted to those
who have completed or are enrolled in Soc. 210, Stat. 265, 311, 402, 405, or 412, or Econ. 404 or 405. (4). (MSA). (BS). (QR/1).
This course is designed to provide an overview of the field of
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. Course evaluation is based on a combination
of a Thursday evening midterm examination, a final examination, and teaching fellow input. The course format includes three lectures
and a laboratory (1 hour per week). Cost:2
WL:3 003: Gunderson)
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Times, Location, and Availability
265/IOE 265. Probability and Statistics for Engineers. Math. 116 and Engin. 101. No credit granted to those who have completed or are enrolled in Stat. 311, 405, or 412, or Econ. 405. (4). (Excl).
Graphical representation of Data; axioms of Probability; conditioning, Bayas Theorem; discreet distributions (Geometric, Binomial, Poisson);
continuous distributions (normal exponential, Weibull), point
and interval estimation, likelihood functions, test of hypotheses
for Means, Variances, and Proportions for one and two popultions.
This course will be taught by both the IOE and Statistics department.
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Times, Location, and Availability
311/IOE 365. Engineering
Statistics. Engin. 101, Math. 215, and IOE 315 or
Stat. 310. No credit granted to those who have completed or are
enrolled in Stat. 265, 405, or 412, or Econ. 405. One credit granted
to those who have completed Stat. 402. (4). (Excl). (BS).
Collection and analysis of engineering data associated with stochastic
industrial processes. Topics include: exploratory data analysis, describing relationships, importance of experimentation, applications
of sampling distribution theory, test of hypotheses, experiments
with one or more factors, and regression analysis. Students are
required to use statistical packages on CAEN for problem solving.
(Majeske)
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Times, Location, and Availability
402. Introduction to
Statistics and Data Analysis. No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 405, or 412. (4). (NS). (BS). (QR/1).
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 two examinations GIVEN WEDNESDAY EVENINGS, a final examination, and teaching fellow input. Cost:2
WL:3 (001: Muirhead, 002: Rothman, 004: Gunderson)
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405/Econ. 405. Introduction
to Statistics. Math. 116 or 118. Juniors and seniors
may elect this course concurrently with Econ. 101 or 102. No credit
granted to those who have completed or are enrolled in Stat. 265, 311 or 412. Students with credit for Econ. 404 can only elect
Stat. 405 for 2 credits and must have permission of instructor.
(4). (MSA). (BS). (QR/1).
See Economics 405. (Howrey)
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Times, Location, and Availability
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 Econ. 405, or Stat. 265, 311, or 405. One credit
granted to those who have completed Stat. 402. (3). (MSA). (BS).
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, two midterms, and a final exam. Cost:2
WL:3
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Times, Location, and Availability
413. The General Linear
Model and Its Applications. Stat. 402 and Math. 217;
concurrent enrollment in Stat. 425. Students who have not taken
Math. 217 should elect Stat. 403. Two credits granted to those
who have completed Stat. 403. (4). (Excl). (BS).
Some motivating real examples – regression, ANOVA, time series
- abstraction into a common model; statement of the model and assumptions; description of the design matrix including dummy
variables; discussion of the error vector and assumptions regarding those errors; geometry of the GLM, including projections, Pythagorus, least squares estimation, residuals, predicted values, Gauss-Markov
result, etc.; normal distribution theory results; confidence
and predictive intervals, F and t tests, the extra sum of squares
principle; multiple and partial correlations with geometry; checking
for violations of the assumptions, normal probability plots, outliers, influence functions, problems of multicollinearity or near collinearity;
cures for violations, transforms, weighting, etc. Multiple
regression applications; choice of independent variables, principal
components, all possible regressions, stepwise procedures, use
of data subsamples (validation); polynomial regression, orthogonal
polynomials. Use of dummy variables and ANOVA applications, fixed
effect completely crossed ANOVA cases, balanced versus unbalanced
designs, contrasts, interactions, multiple inference procedures
including at least Scheffe, studentized range and Bonferroni;
nested designs, etc. Time series applications, use of
polynomial regression, deseasonalization, leads, lags and autoregressive
models, serial correlation, Durbin-Watson test, ARIMA models, etc. Real applications will be stressed. There will be
weekly assignments and a final exam. Class format is three hours
of lecture and 1.5 hours of laboratory per week. Note: This course
is designed primarily for Statistics Undergraduate Concentrators;
other students, without the Mathematics 217 prerequisite, should
elect Statistics 403. Cost:2
WL:3
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Times, Location, and Availability
425/Math. 425. Introduction to Probability. Math. 215, 255, or 285. (3). (MSA). (BS).
Sections 001 and 004 Sample spaces and axiomatic probability; elementary combinatorics; conditional probability and independence; random variables; probability distributions, including binomial, Poisson, Gamma, and normal; expectation, mean and variance; moment generating functions; the law of large numbers; central limit theorem. Cost:2 WL:3
Sections 003 and 005 See Mathematics
425.
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Times, Location, and Availability
426. Introduction to
Mathematical Statistics. Stat. 425. (3). (MSA). (BS).
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. Cost:2
or 3 WL:3
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Times, Location, and Availability
466/IOE 466/Manufacturing
466. Statistical Quality Control. Stat. 265 or 311.
(3). (Excl). (BS).
Design and analysis of procedures for forecasting and control
of production processes. Topics include: attribute and variables
sampling plans; sequential sampling plans; rectifying control
procedures; charting, smoothing, forecasting, and prediction of
discrete time series. Cost:2
WL:3 (Herrin)
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Times, Location, and Availability
470. Experimental Design.
Stat. 402. (4). (Excl). (BS).
This course will introduce students to basic principles in classical
experimental design, including randomization, replication, confounding, interaction, covariates, use of the general linear model. Students
will be introduced to the following designs: completely randomized, randomized blocks, Latin squares, incomplete blocks, factorial, split plot, Taguchi, response surface, optimal. There will be
weekly assignments and a final exam. Class format is 3 hours of
lecture and 1.5 hours of laboratory per week. Cost:2
WL:3
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Times, Location, and Availability
500. Applied Statistics
I. Math. 417, and Stat. 402 or 426. (3). (Excl).
(BS).
Review of matrices, multivariate normal and related distributions.
Regression and general least squares theory, Gauss-Markov Theorem, estimation of regression coefficients, polynomial regression, step-wise regression, residuals. ANOVA models, multiple comparisons, analysis of covariance, Latin squares, 2p factorial
designs, random and mixed-effects models. Applications and real
data analysis will be stressed, with students using a computer
to perform statistical analyses. Cost:2
WL:3 (Faraway)
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Times, Location, and Availability
505/Econ. 673. Econometric
Analysis. Permission of instructor. (3). (Excl).
(BS).
This course is designed for first-year graduate students in economics, business, and related subjects. It involves a fairly rigorous
development of statistical reasoning and methods relating to hypothesis
testing, estimation, and regression analysis. Students are supposed
to have had a course in calculus and in introductory statistics.
Knowledge of matrix algebra is required. Evaluation of students
is based on midterm and final examinations and weekly assignments.
Students taking this course are expected to take Economics 674
- Econometric Analysis II in the following term. Cost:2
WL:3 (Killian)
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Times, Location, and Availability
510. Mathematical Statistics
I. Math. 450 or 451, and a course in probability
or statistics. (3). (Excl). (BS).
Review of probability theory including: probability, conditioning, independence, random variables, standard distributions, exponential
families, inequalities, and the central limit theorem. Introduction
to decision theory including: models, parameter spaces, decision
rules, risk functions, Bayes versus classical approaches, admissibility, minimax rules, likelihood functions and sufficiency. Estimation theory including unbiasedness, complete sufficient statistics, Lehmann-Scheffe and Rao-Blackwell theorems, and various types
of estimators. Cost:3
WL:3
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Times, Location, and Availability
525/Math. 525. Probability
Theory. Math. 450 or 451. Students with credit for
Math. 425/Stat. 425 can elect Math. 525/Stat. 525 for only one
credit. (3). (Excl). (BS).
See Mathematics 525.
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Times, Location, and Availability
535. Reliability. Stat.
425 and 426. (3). (Excl).
This course will cover the important reliability concepts and methodology that arise in modeling, assessing and improving product
reliability and in analyzing field and warranty data. Topics will
be selected from the following: Basic reliability concepts; Common
parametric models for component reliability; Censoring schemes;
Analysis of time-to-failure data; Accelerated testing for reliability
assessment; Modeling and analyzing repairable systems reliability;
Analysis of warranty and field-failure data; Maintenance policies
and availability; Reliability improvement through experimentation.
Cost:2 or 3
WL:3
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Times, Location, and Availability
550/SMS 576 (Business
Administration)/IOE 560. Bayesian Decision Analysis. Stat.
425. (3). (Excl). (BS).
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. Cost:3
WL:3
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Times, Location, and Availability
570. Experimental Design.
Stat. 426 and a basic knowledge of matrix algebra.
(3). (Excl). (BS).
Basic topics and ideas in the design of experiments: randomization
and 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.
Cost:3 or 4
WL:3 (J. Wu)
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Times, Location, and Availability
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