< back 
Send To Printer 
LSA Course Guide Search Results:
UG, GR, Winter 2007, Dept = STATS 
  Page 1 of 1, Results 1 — 53 of 53  

Title
Section
Instructor 
Term
Credits
Requirements 
STATS 100 — Introduction to Statistical Reasoning
Section 001, LEC
Instructor: Gunderson,Brenda K

WN 2007
Credits: 4
Reqs: BS, MSA, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in SOC 210, STATS 265, 350, 400, 405, or 412, IOE 265, or ECON 404 or 405.
Provides an overview of the field of statistics, including methods of summarizing and analyzing data, statistical reasoning for learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Emphasis is on presenting underlying concepts rather than covering a variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, and GSI input. The course format includes lectures and a discussion section (one hour per week).

STATS 100 — Introduction to Statistical Reasoning
Section 003, LEC
Instructor: Namesnik,Kirsten T; homepage

WN 2007
Credits: 4
Reqs: BS, MSA, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in SOC 210, STATS 265, 350, 400, 405, or 412, IOE 265, or ECON 404 or 405.
Provides an overview of the field of statistics, including methods of summarizing and analyzing data, statistical reasoning for learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Emphasis is on presenting underlying concepts rather than covering a variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, and GSI input. The course format includes lectures and a discussion section (one hour per week).

STATS 100 — Introduction to Statistical Reasoning
Section 004, LEC
Instructor: Namesnik,Kirsten T; homepage

WN 2007
Credits: 4
Reqs: BS, MSA, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in SOC 210, STATS 265, 350, 400, 405, or 412, IOE 265, or ECON 404 or 405.
Provides an overview of the field of statistics, including methods of summarizing and analyzing data, statistical reasoning for learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Emphasis is on presenting underlying concepts rather than covering a variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, and GSI input. The course format includes lectures and a discussion section (one hour per week).

STATS 125 — Games, Gambling and Coincidences
Section 001, SEM
Instructor: Keener,Robert W

WN 2007
Credits: 3
Reqs: BS, MSA, QR/1
Other: FYSem 
Emphasizes problem solving and modeling related to games, gambling and coincidences, touching on many fundamental ideas in discrete probability, finite Markov chains, dynamic programming and game theory.
Advisory Prerequisite: Only firstyear students, including those with sophomore standing, may preregister for FirstYear Seminars. All others need permission of instructor.

STATS 265 — Probability and Statistics for Engineers
Section 100, LEC
Instructor: Jin,Jionghua

WN 2007
Credits: 4 
Credit Exclusions: No credit granted to those who have completed or are enrolled in STATS 311, 400, 405, or 412, or ECON 405.
Graphical representation of data; axioms of probability; conditioning, Bayes 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.
Enforced Prerequisites: MATH 116 and ENGR 101 with at least a C

STATS 350 — Introduction to Statistics and Data Analysis
Section 001, LEC
Instructor: Venable Jr,Thomas Calvin

WN 2007
Credits: 4
Reqs: BS, NS, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in ECON 404 or 405, or IOE 265 or STATS 265, 400, 405, or 412.
In this course students are introduced to the concepts and applications of statistical methods and data analysis. STATS 350 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 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 examinations, a final examination, weekly homework, and lab participation.

STATS 350 — Introduction to Statistics and Data Analysis
Section 002, LEC
Instructor: Rothman,Edward D

WN 2007
Credits: 4
Reqs: BS, NS, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in ECON 404 or 405, or IOE 265 or STATS 265, 400, 405, or 412.
In this course students are introduced to the concepts and applications of statistical methods and data analysis. STATS 350 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 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 examinations, a final examination, weekly homework, and lab participation.

STATS 350 — Introduction to Statistics and Data Analysis
Section 003, LEC
Instructor: Gunderson,Brenda K

WN 2007
Credits: 4
Reqs: BS, NS, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in ECON 404 or 405, or IOE 265 or STATS 265, 400, 405, or 412.
In this course students are introduced to the concepts and applications of statistical methods and data analysis. STATS 350 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 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 examinations, a final examination, weekly homework, and lab participation.

STATS 350 — Introduction to Statistics and Data Analysis
Section 004, LEC
Instructor: Venable Jr,Thomas Calvin

WN 2007
Credits: 4
Reqs: BS, NS, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in ECON 404 or 405, or IOE 265 or STATS 265, 400, 405, or 412.
In this course students are introduced to the concepts and applications of statistical methods and data analysis. STATS 350 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 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 examinations, a final examination, weekly homework, and lab participation.

STATS 350 — Introduction to Statistics and Data Analysis
Section 005, LEC
Instructor: Reichert,Heidi Ann

WN 2007
Credits: 4
Reqs: BS, NS, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in ECON 404 or 405, or IOE 265 or STATS 265, 400, 405, or 412.
In this course students are introduced to the concepts and applications of statistical methods and data analysis. STATS 350 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 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 examinations, a final examination, weekly homework, and lab participation.

STATS 350 — Introduction to Statistics and Data Analysis
Section 006, LEC

WN 2007
Credits: 4
Reqs: BS, NS, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in ECON 404 or 405, or IOE 265 or STATS 265, 400, 405, or 412.
A one term course in applied statistical methodology from an analysisofdata viewpoint. Frequency distributions; measures of location; mean, median, mode; measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; one sample univariate inference problems, and two sample problems; categorical data; regression and correlation; and analysis of variance. Use of computers in data analysis.

STATS 401 — Applied Statistical Methods II
Section 001, LEC
Instructor: Namesnik,Kirsten T; homepage

WN 2007
Credits: 4
Reqs: BS, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in STATS 413.
Statistics 401 is an intermediate course in applied statistics, covering a range of topics in modeling and analysis of data including: review of simple linear regression, twosample problems, oneway analysis of variance; multiple linear regression, diagnostics and model selection; twoway analysis of variance, multiple comparisons, and other selected topics. The only prerequisites are statistics 350 (or 400) and math 115.
Lab:
Lab attendance is required. In the lab, the students will be presented with additional examples, learn small amounts of new material, and work on computer problems. Small lab assignments will be completed each week to be graded by the GSI. Students will be free to use any computer package they choose to use, however, in the lab the computer program SPSS will be used for demonstrations.
Textbook:
Applied Linear Statistical Models – Special Statistics 401 Edition,
Neter, Kutner, Nachtsheim, and Wasserman, Irwin (1996), 4th edition.
Homework:
There will be biweekly homework assignments. Assignments will be posted on the website.
Exams:
There will be 2 exams: One midterm and one Final exam.
Grading Policy:
Midterm Exam:
25%
Final Exam:
35%
Homework:
25%
Lab Work:
15%
Advisory Prerequisite: STATS,MATH 115, and STATS 350 or 400 or 405, or ECON 405, or NRE 438. No credit granted to those who have completed or are enrolled in STATS 413

STATS 404 — Effective Communication in Statistics
Section 001, LEC
Instructor: Ritter,Mary A

WN 2007
Credits: 2
Reqs: ULWR, BS 
This course focuses on the principles of good written and oral communication of statistical information and data analyses. Participants study communication principles and apply them in writing assignments and oral presentations of statistical analysis. Topics include giving constructive feedback and rewriting to improve clarity and technical correctness.
Advisory Prerequisite: STAT 470 or 480, and permission of department

STATS 405 — Introduction to Statistics
Section 001, LEC
Instructor: Shin,Yongyun

WN 2007
Credits: 4
Reqs: BS, QR/1 
Credit Exclusions: No credit granted to those who have completed or are enrolled in IOE 265, STATS 265, 400, or 412. Students with credit for ECON 404 can only elect STATS 405/ECON 405 for 2 credits and must have permission of instructor.
The purpose of this course is to provide students with an understanding of the principles of statistical inference. Topics include probability, experimental and theoretical derivation of sampling distributions, hypothesis testing, estimation, and simple regression. (Students are advised to elect the sequel, ECON 406.)
Advisory Prerequisite: MATH 116. Jrs/Srs may elect 405 concurrently with ECON 101 or 102. No credit granted if completed or enrolled in IOE 265, STATS 265, 400, or 412. Students with credit for ECON 404 can only elect 405 for 2 credits and must have permission of instructor.

STATS 408 — Statistical Principles for Problem Solving: A Systems Approach
Section 001, LEC
Instructor: Rothman,Edward D

WN 2007
Credits: 4
Reqs: BS
Other: Honors 
Credit Exclusions: No credit granted to those who have completed or are enrolled in STATS 170.
Our purpose is to help students use quantitative reasoning to facilitate learning. Specifically, we introduce statistical and mathematical principles, and then use these as analogues in a variety of real world situations. The notion of a system, a collection of components that come together repeatedly for a purpose, provides an excellent framework to describe many real world phenomena and provides a way to view the quality of an inferential process.
Evaluation is focused on clear writing that illustrates understanding of the theory by providing new applications of the theory. Points are obtained from four activities: a journal (max 20 points); test score (max 30 points); and discussion section leader bonus (max 5 additional points).
TEXTBOOKS:
 Theory of Constraints, E. Goldratt, Northriver Press;
 The Goal, E. Goldratt, Northriver Press;
 The Fifth Discipline, Peter M. Senge, Doubleday Currency; and
 The New Economics for Industry Government Education, W. Edwards Deming, 2nd Edition, MIT.
Advisory Prerequisite: High school algebra.

STATS 412 — Introduction to Probability and Statistics
Section 001, LEC
Instructor: Shin,Yongyun

WN 2007
Credits: 3
Reqs: BS 
Credit Exclusions: No credit granted to those who have completed or are enrolled in ECON 405, STATS 265, 400, or 405, or IOE 265. One credit granted to those who have completed or are enrolled in STATS 350.
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.
Advisory Prerequisite: Prior or concurrent enrollment in MATH 215

STATS 412 — Introduction to Probability and Statistics
Section 002, LEC
Instructor: Stoev,Stilian Atanasov

WN 2007
Credits: 3
Reqs: BS 
Credit Exclusions: No credit granted to those who have completed or are enrolled in ECON 405, STATS 265, 400, or 405, or IOE 265. One credit granted to those who have completed or are enrolled in STATS 350.
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.
Advisory Prerequisite: Prior or concurrent enrollment in MATH 215

STATS 425 — Introduction to Probability
Section 001, LEC
Instructor: Ziegler,Tamar; homepage

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: This course introduces students to useful and interesting ideas of the mathematical theory of probability and to a number of applications of probability to a variety of fields including genetics, economics, geology, business, and engineering. The theory developed together with other mathematical tools such as combinatorics and calculus are applied to everyday problems. Concepts, calculations, and derivations are emphasized. The course will make essential use of the material of MATH 116 and 215.
Content: Topics include the basic results and methods of both discrete and continuous probability theory: conditional probability, independent events, random variables, jointly distributed random variables, expectations, variances, covariances. Different instructors will vary the emphasis.
Alternatives: MATH 525 (Probability Theory) is a similar course for students with stronger mathematical background and ability.
Subsequent Courses: STATS 426 (Intro. To Math. Stat.) is a natural sequel for students. MATH 423 (Mathematics of Finance) and MATH 523 (Risk Theory) include many applications of probability theory.
Advisory Prerequisite: MATH 215

STATS 425 — Introduction to Probability
Section 002, LEC
Instructor: Ziegler,Tamar; homepage

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: This course introduces students to useful and interesting ideas of the mathematical theory of probability and to a number of applications of probability to a variety of fields including genetics, economics, geology, business, and engineering. The theory developed together with other mathematical tools such as combinatorics and calculus are applied to everyday problems. Concepts, calculations, and derivations are emphasized. The course will make essential use of the material of MATH 116 and 215.
Content: Topics include the basic results and methods of both discrete and continuous probability theory: conditional probability, independent events, random variables, jointly distributed random variables, expectations, variances, covariances. Different instructors will vary the emphasis.
Alternatives: MATH 525 (Probability Theory) is a similar course for students with stronger mathematical background and ability.
Subsequent Courses: STATS 426 (Intro. To Math. Stat.) is a natural sequel for students. MATH 423 (Mathematics of Finance) and MATH 523 (Risk Theory) include many applications of probability theory.
Advisory Prerequisite: MATH 215

STATS 425 — Introduction to Probability
Section 003, LEC
Instructor: Dean,Carolyn A; homepage

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: This course introduces students to useful and interesting ideas of the mathematical theory of probability and to a number of applications of probability to a variety of fields including genetics, economics, geology, business, and engineering. The theory developed together with other mathematical tools such as combinatorics and calculus are applied to everyday problems. Concepts, calculations, and derivations are emphasized. The course will make essential use of the material of MATH 116 and 215.
Content: Topics include the basic results and methods of both discrete and continuous probability theory: conditional probability, independent events, random variables, jointly distributed random variables, expectations, variances, covariances. Different instructors will vary the emphasis.
Alternatives: MATH 525 (Probability Theory) is a similar course for students with stronger mathematical background and ability.
Subsequent Courses: STATS 426 (Intro. To Math. Stat.) is a natural sequel for students. MATH 423 (Mathematics of Finance) and MATH 523 (Risk Theory) include many applications of probability theory.
Advisory Prerequisite: MATH 215

STATS 425 — Introduction to Probability
Section 004, LEC
Instructor: Amirdjanova,Anna

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: This course introduces students to useful and interesting ideas of the mathematical theory of probability and to a number of applications of probability to a variety of fields including genetics, economics, geology, business, and engineering. The theory developed together with other mathematical tools such as combinatorics and calculus are applied to everyday problems. Concepts, calculations, and derivations are emphasized. The course will make essential use of the material of MATH 116 and 215.
Content: Topics include the basic results and methods of both discrete and continuous probability theory: conditional probability, independent events, random variables, jointly distributed random variables, expectations, variances, covariances. Different instructors will vary the emphasis.
Alternatives: MATH 525 (Probability Theory) is a similar course for students with stronger mathematical background and ability.
Subsequent Courses: STATS 426 (Intro. To Math. Stat.) is a natural sequel for students. MATH 423 (Mathematics of Finance) and MATH 523 (Risk Theory) include many applications of probability theory.
Advisory Prerequisite: MATH 215

STATS 425 — Introduction to Probability
Section 005, LEC
Instructor: Atchade,Yves A

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: This course introduces students to useful and interesting ideas of the mathematical theory of probability and to a number of applications of probability to a variety of fields including genetics, economics, geology, business, and engineering. The theory developed together with other mathematical tools such as combinatorics and calculus are applied to everyday problems. Concepts, calculations, and derivations are emphasized. The course will make essential use of the material of MATH 116 and 215.
Content: Topics include the basic results and methods of both discrete and continuous probability theory: conditional probability, independent events, random variables, jointly distributed random variables, expectations, variances, covariances. Different instructors will vary the emphasis.
Alternatives: MATH 525 (Probability Theory) is a similar course for students with stronger mathematical background and ability.
Subsequent Courses: STATS 426 (Intro. To Math. Stat.) is a natural sequel for students. MATH 423 (Mathematics of Finance) and MATH 523 (Risk Theory) include many applications of probability theory.
Advisory Prerequisite: MATH 215

STATS 425 — Introduction to Probability
Section 006, LEC
Instructor: Thelen,Brian J; homepage

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: This course introduces students to useful and interesting ideas of the mathematical theory of probability and to a number of applications of probability to a variety of fields including genetics, economics, geology, business, and engineering. The theory developed together with other mathematical tools such as combinatorics and calculus are applied to everyday problems. Concepts, calculations, and derivations are emphasized. The course will make essential use of the material of MATH 116 and 215.
Content: Topics include the basic results and methods of both discrete and continuous probability theory: conditional probability, independent events, random variables, jointly distributed random variables, expectations, variances, covariances. Different instructors will vary the emphasis.
Alternatives: MATH 525 (Probability Theory) is a similar course for students with stronger mathematical background and ability.
Subsequent Courses: STATS 426 (Intro. To Math. Stat.) is a natural sequel for students. MATH 423 (Mathematics of Finance) and MATH 523 (Risk Theory) include many applications of probability theory.
Advisory Prerequisite: MATH 215

STATS 425 — Introduction to Probability
Section 007, LEC
Instructor: Petrakiev,Ivan Georgiev; homepage

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: This course introduces students to useful and interesting ideas of the mathematical theory of probability and to a number of applications of probability to a variety of fields including genetics, economics, geology, business, and engineering. The theory developed together with other mathematical tools such as combinatorics and calculus are applied to everyday problems. Concepts, calculations, and derivations are emphasized. The course will make essential use of the material of MATH 116 and 215.
Content: Topics include the basic results and methods of both discrete and continuous probability theory: conditional probability, independent events, random variables, jointly distributed random variables, expectations, variances, covariances. Different instructors will vary the emphasis.
Alternatives: MATH 525 (Probability Theory) is a similar course for students with stronger mathematical background and ability.
Subsequent Courses: STATS 426 (Intro. To Math. Stat.) is a natural sequel for students. MATH 423 (Mathematics of Finance) and MATH 523 (Risk Theory) include many applications of probability theory.
Advisory Prerequisite: MATH 215

STATS 425 — Introduction to Probability
Section 008, LEC
Instructor: Smereka,Peter S; homepage

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: This course introduces students to useful and interesting ideas of the mathematical theory of probability and to a number of applications of probability to a variety of fields including genetics, economics, geology, business, and engineering. The theory developed together with other mathematical tools such as combinatorics and calculus are applied to everyday problems. Concepts, calculations, and derivations are emphasized. The course will make essential use of the material of MATH 116 and 215.
Content: Topics include the basic results and methods of both discrete and continuous probability theory: conditional probability, independent events, random variables, jointly distributed random variables, expectations, variances, covariances. Different instructors will vary the emphasis.
Alternatives: MATH 525 (Probability Theory) is a similar course for students with stronger mathematical background and ability.
Subsequent Courses: STATS 426 (Intro. To Math. Stat.) is a natural sequel for students. MATH 423 (Mathematics of Finance) and MATH 523 (Risk Theory) include many applications of probability theory.
Advisory Prerequisite: MATH 215

STATS 426 — Introduction to Theoretical Statistics
Section 001, LEC
Instructor: Banerjee,Moulinath

WN 2007
Credits: 3
Reqs: 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 STATS 425/426 serves as a prerequisite for more advanced Statistics courses, regular homework and a final exam.
Random Variables
Joint Distributions
Induced Distributions
Expectation
The Law of Large Numbers
The Central Limit Theorem
Simulation
Populations and Samples
The Chisquared, t, and F Distributions
Estimation: The Method of Moments
Maximum Likelihood Estimation
More on Maximum Likelihood Estimation
Bias, Variance, and MSE
The Cramer Rao Inequality
Exponential Families and Sufficiency
Confidence Intervals
Approximate Confidence Intervals
The Bootstrap
Asymptotics of the MLE
Tests and Confidence Intervals
Neyman Pearson
Likelihood Ratio Tests
ChiSquared Tests
Goodness of Fit Tests
The Sample Distribution Function
Decision Analysis
Bayesian Inference
The Two Sample Problem
More on the Two Sample Problem
Rank Tests
One Way ANOVA
Simultaneous Confidence
Two Way ANOVA
Categorical Data
Simple Linear Regression
Multiple Regression
Advisory Prerequisite: STATS 425.

STATS 449 — Topics in Biostatistics
Section 001, LEC
Instructor: Venable Jr,Thomas Calvin

WN 2007
Credits: 3
Reqs: BS 
An introduction to biostatistical topics: clinical trials, cohort and casecontrol studies; experimental versus observational data; issues of causation, randomization, placebos; case control studies; survival analysis; diagnostic testing; image analysis of PET and MRI scans; statistical genetics; longitudinal studies; and missing data. The course stresses both the development of theory and methods and the applications to real case studies and examples in the biomedical sciences.
Advisory Prerequisite: STATS 401 or permission of instructor.

STATS 466 — Statistical Quality Control
Section 001, LEC
Instructor: GarciaGuzman,Luis Manuel
Instructor: Suriano,Saumuy

WN 2007
Credits: 3 
Quality improvement philosophies; Modeling process quality, statistical process control, control charts for variables and attributes, CUSUM and EWMA, short production runs, multivariate quality control, auto correlation, engineering process control economic design of charts, fill control, precontrol, adaptive schemes, process capability, specifications and tolerances, gage capability studies, acceptance sampling by attributes and variables, international quality standards.
Enforced Prerequisites: STATS 265 and 401 or IOE 366, with at least a C; or graduate standing.

STATS 480 — Survey Sampling Techniques
Section 001, LEC
Instructor: Hansen,Bendek B

WN 2007
Credits: 4
Reqs: BS 
Introduces 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 design (simple random, cluster, systematic, multiple stage), various errors and biases, special topics.
Enforced Prerequisites: STATS 401 or 412 or 425 or MATH 425

STATS 489 — Independent Study in Statistics
Section 001, IND

WN 2007
Credits: 1 — 4 
Individual study of advanced topics in statistics, reading and/or research in applied or theoretical statistics.
Advisory Prerequisite: Permission of instructor

STATS 499 — Honors Seminar
Section 001, IND

WN 2007
Credits: 2 — 3
Reqs: BS
Other: Honors, Indpnt Study 
Advanced topics, reading and/or research in applied or theoretical statistics.
Advisory Prerequisite: Permission of departmental Honors advisor.

STATS 500 — Applied Statistics I
Section 001, LEC
Instructor: Levina,Elizaveta; homepage

WN 2007
Credits: 3
Reqs: BS 
Course outline: Course Outline:
Linear Regression Models: deØnition, Øtting, GaussMarkov theorem, inference,
interpretation of results, meaning of regression coe±cients, identiØablity,
diagnostics, in°uential observations, multicollinearity, lack of Øt,
robust procedures, transformations, regression splines, variable selection,
ridge regression, principal components regression, ANOVA and analysis of
covariance. The objective is to learn what methods are available and more
importantly, when they should be applied.
Computing: The software we will be using for this course is R. R is free with Windows
and Unix. Check out the course webpage for details.
Textbook:
Julian Faraway (2004) Linear Models with R. Chapman & Hall.
Prerequisites:
Knowledge of matrix algebra. Knowledge of basic probability and mathematical
statistics.
Assessment:
There will be weekly homeworks, one midterm, and the final. The weights
are 30% for the homework, 30% for the midterm and 40% for the final. No
late homework will be accepted. No makeup exam.
Advisory Prerequisite: STATS,MATH 417, and STATS 350 or 426.

STATS 500 — Applied Statistics I
Section 002, LEC
Instructor: Levina,Elizaveta; homepage

WN 2007
Credits: 3
Reqs: BS 
Linear models: definitions, fitting, identifiability, collinearity, GaussMarkov theorem, variable selection, transformation, diagnostics, outliers and influential observations. ANOVA and ANCOVA. Common Designs. Applications and real data analysis are stressed, with students using the computer to perform statistical analyses.
Advisory Prerequisite: STATS,MATH 417, and STATS 350 or 426.

STATS 503 — Applied Multivariate Analysis
Section 001, LEC
Instructor: Michailidis,George

WN 2007
Credits: 3
Reqs: BS 
Topics in applied multivariate analysis including Hotelling's Tsquared, multivariate ANOVA, discriminant functions, factor analysis, principal components, canonical correlations, and cluster analysis. Selected topics from: maximum likelihood and Bayesian methods, robust estimation and survey sampling. Applications and data analysis using the computer is stressed.
Advisory Prerequisite: STATS. 500 or permission of instructor.

STATS 525 — Probability Theory
Section 001, LEC
Instructor: Fomin,Sergey; homepage

WN 2007
Credits: 3
Reqs: BS 
This course is a thorough and fairly rigorous study of the mathematical theory of probability. There is substantial overlap with MATH 425, but here more sophisticated mathematical tools are used and there is greater emphasis on proofs of major results. MATH 451 is preferable to MATH 450 as preparation, but either is acceptable. Topics include the basic results and methods of both discrete and continuous probability theory. Different instructors will vary the emphasis between these two theories. EECS 501 also covers some of the same material at a lower level of mathematical rigor. MATH 425 is a course for students with substantially weaker background and ability. MATH 526, STATS 426, and the sequence STATS 610611 are natural sequels.
Advisory Prerequisite: STATS,MATH 451 (strongly recommended). MATH 425/STATS 425 would be helpful.

STATS 526 — Discrete State Stochastic Processes
Section 001, LEC
Instructor: Ludkovski,Michael; homepage

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: The theory of stochastic processes is concerned with systems which change in accordance with probability laws. It can be regarded as the 'dynamic' part of statistic theory. Many applications occur in physics, engineering, computer sciences, economics, financial mathematics and biological sciences, as well as in other branches of mathematical analysis such as partial differential equations. The purpose of this course is to provide an introduction to the many specialized treatise on stochastic processes. Most of this course is on discrete state spaces. It is a second course in probability which should be of interest to students of mathematics and statistics as well as students from other disciplines in which stochastic processes have found significant applications. Special efforts will be made to attract and interest students in the rich diversity of applications of stochastic processes and to make them aware of the relevance and importance of the mathematical subtleties underlying stochastic processes.
Content: The material is divided between discrete and continuous time processes. In both, a general theory is developed and detailed study is made of some special classes of processes and their applications. Some specific topics include generating functions; recurrent events and the renewal theorem; random walks; Markov chains; limit theorems; Markov chains in continuous time with emphasis on birth and death processes and queueing theory; an introduction to Brownian motion; stationary processes and martingales. Significant applications will be an important feature of the course.
Coursework: weekly or biweekly problem sets and a midterm exam will each count for 30% of the grade. The final will count for 40%.
Advisory Prerequisite: MATH 525 or EECS 501

STATS 526 — Discrete State Stochastic Processes
Section 002, LEC
Instructor: Egami,Masahiko; homepage

WN 2007
Credits: 3
Reqs: BS 
Background and Goals: The theory of stochastic processes is concerned with systems which change in accordance with probability laws. It can be regarded as the 'dynamic' part of statistic theory. Many applications occur in physics, engineering, computer sciences, economics, financial mathematics and biological sciences, as well as in other branches of mathematical analysis such as partial differential equations. The purpose of this course is to provide an introduction to the many specialized treatise on stochastic processes. Most of this course is on discrete state spaces. It is a second course in probability which should be of interest to students of mathematics and statistics as well as students from other disciplines in which stochastic processes have found significant applications. Special efforts will be made to attract and interest students in the rich diversity of applications of stochastic processes and to make them aware of the relevance and importance of the mathematical subtleties underlying stochastic processes.
Content: The material is divided between discrete and continuous time processes. In both, a general theory is developed and detailed study is made of some special classes of processes and their applications. Some specific topics include generating functions; recurrent events and the renewal theorem; random walks; Markov chains; limit theorems; Markov chains in continuous time with emphasis on birth and death processes and queueing theory; an introduction to Brownian motion; stationary processes and martingales. Significant applications will be an important feature of the course.
Coursework: weekly or biweekly problem sets and a midterm exam will each count for 30% of the grade. The final will count for 40%.
Advisory Prerequisite: MATH 525 or EECS 501

STATS 531 — Analysis of Time Series
Section 001, LEC
Instructor: Ionides,Edward L; homepage

WN 2007
Credits: 3
Reqs: BS 
Decomposition of series; trends and regression as a special case of time series; cyclic components; smoothing techniques; the variate difference method; representations including spectrogram, periodogram, etc.; stochastic difference equations, autoregressive schemes, moving averages; large sample inference and prediction; covariance structure and spectral densities; hypothesis testing and estimation and applications and other topics.
Advisory Prerequisite: STATS 426.

STATS 545 — Data Analysis in Molecular Biology
Section 001, LEC
Instructor: Ghosh,Debashis

WN 2007
Credits: 3
Reqs: BS 
This course will cover statistical methods used to analyze data in experimental molecular biology, with an emphasis on gene and protein expression array data. Topics: data acquisition, databases, low level processing, normalization, quality control, statistical inference (group comparisons, cyclicity, survival), multiple comparisons, statistical learning algorithms, clustering visualization, and case studies.
Advisory Prerequisite: STATS,Graduate standing and STATS 400 (or equivalent)/permission of instructor. Students should have a strong preparation in either biology or some branch of quantitative analysis (mathematics, statistics, or computer science).

STATS 575 — Advanced Econometrics I
Section 001, LEC
Instructor: Lee,Yoonseok

WN 2007
Credits: 3
Reqs: BS 
This is a course in econometric theory introducing the statistical foundation of the nonlinear and nonparametric/semiparametric models in econometrics. The course involves a development of the asymptotic distribution theory in depth. Selected current research topics will be also covered depending on time and interest.
The instructor recommends the following prerequisites: ECON 600, 671 and 672 or their equivalents.
Advisory Prerequisite: STATS,MATH 417 and 425 or ECON 671,672, and 600.

STATS 601 — Analysis of Multivariate and Categorical Data
Section 001, LEC
Instructor: Michailidis,George

WN 2007
Credits: 3 
This is an advanced introduction to the analysis of multivariate and categorical data. Topics include: 1) dimensional reduction techniques, including principal component analysis, multidimensional scaling and extensions; 2) classification, starting with a conceptual framework developed from cost functions, Bayes classifiers, and issues of overfitting and generalization, and continuing with a discussion of specific classification methods, including LDA, QDA, and KNN; 3) discrete data analysis, including estimation and testing for loglinear models and contingency tables; 4) largescale multiple hypothesis testing, including Bonferroni, WestphalYoung and related approaches, and false discovery rates; 5) shrinkage and regularization, including ridge regression, principal component regression, partial least squares, and the lasso; 6) clustering methods, including hierarchical methods, partitioning methods, Kmeans, and model based clustering.
Advisory Prerequisite: STATS 600

STATS 611 — Large Sample Theory
Section 001, LEC
Instructor: Keener,Robert W

WN 2007
Credits: 3 
More on the theory of estimation including: minimax, Bayes and JamesStein estimators. The theory of hypothesis testing including: Tests significance levels, power, the NeymanPearson lemma, uniformly most powerful unbiased tests, monotone likelihood rations, locally best tests, similar tests, likelihood ratio tests and the associated large sample theory, sequential tests, goodness of fit tests, and tests in contingency tables. Other topics include: confidence regions, introduction tot eh general linear model and nonparametric methods.
Advisory Prerequisite: STAT 610 and Graduate standing.

STATS 617 — Advanced Topic in Quantitative Methodology
Section 001, LEC
causal inference in the social sciences
Instructor: Xie,Yu; homepage

WN 2007
Credits: 3 
In this course we explore and critique methods for conducting causal inference in the social sciences. These methods will be drawn from a wide variety of disciplines, including economics, sociology, statistics, education, psychology, and epidemiology. Particular attention will be paid to causal inference from quasiexperimental and observational research designs. This course is part of the Michigan Methodology Seminar. It provides an interdisciplinary forum for researchers and graduate students in several related disciplines at Michigan to be engaged in discussing cuttingedge issues in social science methodology.
Advisory Prerequisite: STATS,Graduate level courses in Statistics at the level of 500 and 501 or permission of instructor

STATS 621 — Probability Theory
Section 001, LEC
Instructor: Amirdjanova,Anna

WN 2007
Credits: 3 
A continuation of STATS 620. Topics covered include: weak convergence, characteristic functions, inversion, unicity and continuity, the central limit theorem for sequences and arrays aud, extensions to higher dimensions. Also: the renewal theorem, conditional probability and expectation, regular conditional distributions, stationary sequences aud the bergodic theorem, martingales, and the optimal stopping theorem. The course will also cover: the Poisson process, Brownian motion, the strong Markov property and the invariance principle.
Advisory Prerequisite: STATS,STATS 520 or equivalent course in measure theory, STATS 620

STATS 625 — Probability and Random Processes I
Section 002, LEC
Instructor: Bayraktar,Erhan; homepage

WN 2007
Credits: 3
Reqs: BS 
A graduate level introduction to probability theory and stochastic processes: measure theory and integration; Kolmogorov Extension Theorem; conditional expectation; characteristic functions; convergence concepts; limit theorems; stochastic processes, Poisson random measures, Brownian motion, Levy processes and martingales.
Required Textbooks
Book 1:
AUTHOR: Williams
TITLE: Probability with Martingales
ISBN: 0521406056
BINDING: paper
PUBLISHER: Cambridge University Press
Book 2:
AUTHOR: Protter and Jacod
TITLE: Probability Essentials
ISBN: 3540438718
BINDING: paper
PUBLISHER: Springer
Recommended Textbooks
Book 1:
AUTHOR: Varadhan
TITLE: Probability Theory
ISBN: 0821828525
BINDING: paper
PUBLISHER: Courant Institute of Mathemetical Sciences
Book 2:
AUTHOR: Shiryaev
TITLE: Probability
ISBN: 0387945490
BINDING: hard
PUBLISHER: Springer
(Tentative) Syllabus:
Measure and Integration
1) Measurable Spaces January 5
2) Measurable Functions 8
3) Measures 10
4) Integration 12
5) Radon Nikodym Theorem 17
6) Kernels and Product Spaces 19
Probability Spaces
7) Probability Spaces and Random Var. 22
8) Expectations 24
9) L^p Spaces 26
10) Information and Determinability 29
11) Independence 31
Convergence
12) Almost Sure Convergence February 2
13) Convergence in Probability 5
14) Convergence in L^p 7
15) Weak Convergence 9
16) Laws of Large Numbers 12
17) Convergence of Series 14
18) Central Limit Theorems 16
Conditioning
19) Conditional Expectations 19
20) Conditional Probabilities and Distributions 21
21) Midterm 23
22) Construction of Probability Spaces March 5
23) Special Constructions 7
Martingales
24) Filtrations and Stopping Times 9
25) Martingales 12
26) Martingale Transformations 14
27) Martingale Convergence 16, 19
28) Martingales in Continuous Time 21
29) Martingale Inequalities 23
30) Martingale Characterizations of Wiener 26
and Poisson
31) Regularity of Filtrations 28
Poisson Random Measures
32) Random Measures 30
33) Poisson Random Measures April 2, 4
34) Transforms and Magnifications 6
35) Levy Random Measures 9
36) Poisson Processes 11
37) Levy Processes 13
38) Review 16
Advisory Prerequisite: MATH 597 and Graduate standing.

STATS 630 — Topics in Applied Probability
Section 001, LEC
Instructor: Amirdjanova,Anna

WN 2007
Credits: 3 
Advanced topics in applied probability, such as queueing theory, inventory problems, branching processes, stochastic difference and differential equations, etc. The course will study one or two advanced topics in detail.
Advisory Prerequisite: STATS 626 and Graduate standing.

STATS 711 — Special Topics in Theoretical Statistics II
Section 001, LEC
Instructor: Murphy,Susan A; homepage

WN 2007
Credits: 3 
Special topics.
Advisory Prerequisite: Graduate standing and permission of instructor.

STATS 811 — Literature Proseminar II
Section 001, SEM
Instructor: Hansen,Bendek B

WN 2007
Credits: 2 
This course is designed to acquaint students with classical papers in mathematical and applied statistics and probability theory, to encourage them in critical independent reading, and to permit them to gain pedagogical experience during the course of their graduate training.
Advisory Prerequisite: Graduate standing and permission of instructor.

STATS 817 — Interdisciplinary Seminar in Quantitative Social Science Methodology
Section 001, SEM
Instructor: Xie,Yu; homepage

WN 2007
Credits: 1 
This seminar considers methodological issues that arise in research in the social sciences. Themes arise from ongoing research projects at the UM. Visiting researchers provide a brief account of their aims and data before defining the methodological challenges for which they desire discussion.
Advisory Prerequisite: Graduate standing, and Graduatelevel course in STATS at the level of STAT 500 and 501.

STATS 819 — Seminar in Theoretical Statistics II
Section 001, SEM
Instructor: Stoev,Stilian Atanasov

WN 2007
Credits: 1 
A seminar on topics in theoretical statistics. Content varies by term and instructor.
Advisory Prerequisite: Graduate standing.

STATS 990 — Dissertation/Precandidate
Section 001, IND

WN 2007
Credits: 1 — 8 
Election for dissertation work by doctoral student not yet admitted as a candidate.
Advisory Prerequisite: Election for dissertation work by doctoral student not yet admitted as a Candidate. Graduate standing.

STATS 993 — Graduate Student Instructor Training Program
Section 001, REC
Instructor: Gunderson,Brenda K

WN 2007
Credits: 1 
A seminar for all beginning graduate student instructors, consisting of a twoday orientation before the term starts and periodic workshops/meetings during the term. Beginning graduate student instructors are required to register for this course.
Advisory Prerequisite: Graduate standing.

STATS 995 — Dissertation/Candidate
Section 001, IND

WN 2007
Credits: 8 
Graduate school authorization for admission as a doctoral candidate. N.B. The defense of the dissertation (the final oral examination) must be held under a full term candidacy enrollment period.
Enforced Prerequisites: Graduate School authorization for admission as a doctoral Candidate

  Page 1 of 1, Results 1 — 53 of 53  

