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Open courses in Statistics
Wolverine Access Subject listing for STATS
Take me to the Winter Term '01 Time Schedule for Statistics.
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STATS 100. Introduction to Statistical Reasoning.
Section – MIDTERM EXAM ON THURSDAY,FEBRUARY 22, 68 P.M.
Instructor(s): Brenda Gunderson (bkg@umich.edu)
Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Soc. 210, Stat. 250, 402, 405, or 412, or Econ. 404 or 405. (4). (MSA). (BS). (QR/1).
Credits: (4).
Course Homepage: No Homepage Submitted.
This course will provide an overview of the field of Statistics. It will expose students to statistical reasoning and concepts for dealing with uncertainty, visualizing and analyzing data, and drawing conclusions from experimental or observational data. Emphasis is on presenting underlying concepts rather than covering a variety of different methodologies. Course evaluation is based on a combination of inclass quizzes, weekly homework, a Thursday evening midterm examination, a final examination, and GSI input. The course format includes lectures and a discussion section (1 hour per week).
STATS 125. Games, Gambling and Coincidences.
Section 001.
Prerequisites & Distribution: Only firstyear students, including those with sophomore standing, may preregister for FirstYear Seminars. All others need permission of instructor. (3). (MSA). (QR/1).
FirstYear Seminar,
Credits: (3).
Course Homepage: No Homepage Submitted.
This course will emphasize problem solving and modeling. Students will work together in class attempting to solve various problems. With guidance from the instructor, students will create models and deduce the basic concepts necessary for solution. Students will be asked to write up solutions and work on a project. Grades will be determined from this work and class participation. Problems from the course will be drawn primarily from Markov chains with a finite state space, dynamic programming, again with a finite state space, and game theory. Possible examples include: gambler's ruin; expected run lengths in coin tossing until a specified string is obtained and chances that one string will occur before another; optimal strategies in sports and gambling; optimal replacement strategies; minimax solutions for finite state twoperson zero sum games.
STATS 170. The Art of Scientific Investigation.
Section 001.
Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Statistics 408. (4). (MSA). (BS). (QR/1).
Credits: (4).
Course Homepage: No Homepage Submitted.
This course will explore the critical thought processes involved in a scientific investigation. Concepts covered will include: the role of empiricism, modeling, the nature of variability, the design of scientific experiments (advantages and disadvantages), the role of randomization, the measurement process, possible biases, the use of controls, and the evaluation of final results. Examples from the history of science will be used to illustrate successes and failures in science and various ethical issues will be considered. The course format includes three lectures and a laboratory (1.5 hours per week).
STATS 250(402). Introduction to Statistics and Data Analysis.
Section – TWO (2) WEDNESDAY EVENING MIDTERM EXAMINATIONS FOR STATISTICS 402 ON 02/14 AND 3/28, 68 P.M.
Instructor(s): Brenda Gunderson (bkg@umich.edu)
Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 400, 402, 405, or 412. (4). (NS). (BS). (QR/1).
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/stat250/
In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 250 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 subjectmatter. The course format includes lectures (3 hours per week) and a laboratory (1.5 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 statistical analysiscomputer package. Course evaluation is based on a combination of two midterm examinations, a final examination, weekly homework, and lab participation.
This course was previously Stats 402. Advanced undergraduates and graduate students should take the new course, Stats 400.
STATS 265/IOE 265. Probability and Statistics for Engineers.
Section 001.
Instructor(s): Pierre Goovaerts
Prerequisites & Distribution: 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). (BS). CAEN lab access fee required for nonEngineering students.
Credits: (4).
Lab Fee: CAEN lab access fee required for nonEngineering students.
Course Homepage: No Homepage Submitted.
Graphical representation of Data; axioms of Probability; conditioning, Bayas Theorem; discrete 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 populations. This course will be taught by both the IOE and Statistics department.
STATS 400. Applied Statistical Methods.
Prerequisites & Distribution: High School Algebra. No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 250, 265, 402, 405, or 412. (4). (Excl). (BS).
Credits: (4).
Course Homepage: No Homepage Submitted.
This course is aimed at advanced undergraduate students and graduate students from disciplines outside of Statistics. The course will introduce students to a broad range of applied statistical methods involved in data collection, analysis and visualization. Emphasis will be placed on using statistical methods to answer realworld problems. Statistics and the scientific method; observational study versus designed experiment; visualization; introduction to probability; statistical inference; confidence intervals; onesample tests of hypothesis; twosample problems; analysis of variance (ANOVA); blocked designs; tests for association and independence (chisquare tests); regression and correlation; and nonparametric tests. Course format includes lectures (3 hours per week) and a laboratory (1.5 hours per week).
STATS 403. Introduction to Statistics and Data Analysis II.
Section 001.
Prerequisites & Distribution: Stat. 250 (or 402). (4). (Excl). (BS).
Credits: (4).
Course Homepage: No Homepage Submitted.
Intermediate topics in multiple linear regression and the analysis of variance, stressing applications: least squares estimates, test of hypotheses, prediction analysis, residual analysis, multicollinearity, and the variable selection techniques; fixed and random effects models in ANOVA; multiple comparisons, randomized blocks, Latin squares, nested and hierarchical designs; and robust procedures, as time permits. Three hours of lecture supplemented by one and onehalf hours of laboratory.
STATS 405/Econ. 405. Introduction to Statistics.
Section 001.
Prerequisites & Distribution: 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).
Credits: (4).
Course Homepage: No Homepage Submitted.
See Economics 405.001.
STATS 408. Statistical Principles for Problem Solving: A Systems Approach.
Prerequisites & Distribution: High School Algebra. No credit granted to those who have completed or are enrolled in Statistics 170. (4). (Excl). (BS).
Credits: (4).
Course Homepage: No Homepage Submitted.
No Description Provided.
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STATS 412. Introduction to Probability and Statistics.
Section 001.
Instructor(s): P. Jegenathan (jegan@umich.edu)
Prerequisites & Distribution: 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, 400, or 405. One credit granted to those who have completed Stat. 250 or 402. (3). (MSA). (BS).
Credits: (3).
Course Homepage: No Homepage Submitted.
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.
STATS 425/Math. 425. Introduction to Probability.
Prerequisites & Distribution: Math. 215, 255, or 285. (3). (MSA). (BS).
Credits: (3).
Course Homepage: No Homepage Submitted.
No Description Provided.
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STATS 426. Introduction to Mathematical Statistics.
Section 001.
Prerequisites & Distribution: Stat. 425. (3). (MSA). (BS).
Credits: (3).
Course Homepage: No Homepage Submitted.
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, regular homework and a final exam.
STATS 430. Applied Probability.
Section 001.
Prerequisites & Distribution: Stats. 425. (3). (Excl). (BS).
Credits: (3).
Course Homepage: No Homepage Submitted.
No Description Provided.
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STATS 466/IOE 466/Manufacturing 466. Statistical Quality Control.
Section 001.
Prerequisites & Distribution: Stat. 265 and Stat 403 or IOE 366. (4). (Excl). (BS). CAEN lab access fee required for nonEngineering students.
Credits: (4).
Lab Fee: CAEN lab access fee required for nonEngineering students.
Course Homepage: No Homepage Submitted.
No Description Provided.
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STATS 480. Survey Sampling Techniques.
Section 001.
Prerequisites & Distribution: Stat. 250 (or 402). (4). (Excl). (BS).
Credits: (4).
Course Homepage: No Homepage Submitted.
Course will introduce students to basic ideas in survey sampling, moving from motivating examples to abstraction to populations, variables, parameters, samples and sample design, statistics, sampling distributions, HorvitzThompson estimators, basic sample designs (simple random, cluster, systematic, stratified, multiple state), various errors and biases, special topics. Three hours lecture and 1.5 hour laboratory session each week.
STATS 499. Honors Seminar.
Prerequisites & Distribution: Permission of departmental Honors advisor. (23). (Excl). (INDEPENDENT).
Credits: (23).
Course Homepage: No Homepage Submitted.
Advanced topics, reading and/or research in applied or theoretical statistics.
STATS 501. Applied Statistics II.
Section 001.
Prerequisites & Distribution: Stat. 500. (3). (Excl). (BS).
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~faraway/stat501/
Generalized Linear Models, Analysis of binary and categorical data, Loglinear models, Random and mixed effects models, Smoothing and nonparametric regression, Generalized Additive models, Regression and classification trees, Neural Networks.
STATS 511. Mathematical Statistics II.
Section 001.
Prerequisites & Distribution: Stat. 510. (3). (Excl). (BS).
Credits: (3).
Course Homepage: No Homepage Submitted.
No Description Provided.
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STATS 525/Math. 525. Probability Theory.
Section 001.
Prerequisites & Distribution: 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).
Credits: (3).
Course Homepage: No Homepage Submitted.
See Mathematics 525.001.
STATS 526/Math. 526. Discrete State Stochastic Processes.
Section 001.
Prerequisites & Distribution: Stat. 525 or EECS 501. (3). (Excl). (BS).
Credits: (3).
Course Homepage: No Homepage Submitted.
No Description Provided.
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STATS 531/Econ. 677. Analysis of Time Series.
Section 001.
Prerequisites & Distribution: Stat. 426. (3). (Excl). (BS).
Credits: (3).
Course Homepage: No Homepage Submitted.
Models and methods for univariate and multivariate discrete
time stochastic processes; estimation, testing, and forecasting; frequency domain methods; applications of the Kalman filter.
This page was created at 9:39 AM on Wed, Nov 1, 2000.
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