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LSA Course Guide Search Results: UG, GR, Fall 2007, Dept = STATS
 
Page 1 of 1, Results 1 — 52 of 52
Title
Section
Instructor
Term
Credits
Requirements
STATS 100 — Introduction to Statistical Reasoning
Section 001, LEC

Instructor: Venable Jr,Thomas Calvin

FA 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, or NRE 438 (or ENVIRON 438).

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 in class quizzes, homework, an 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 002, LEC

Instructor: Venable Jr,Thomas Calvin

FA 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, or NRE 438 (or ENVIRON 438).

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 in class quizzes, homework, an evening midterm examination, a final examination, and GSI input. The course format includes lectures and a discussion section (one hour per week).

STATS 265 — Probability and Statistics for Engineers
Section 001, LEC

Instructor: Herrin,Gary D

FA 2007
Credits: 4

Credit Exclusions: A student can receive credit for only one of the following: STATS 350, 400, 405, or 412, or ECON 404 or 405, or NRE 438 (or ENVIRON 438).

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

FA 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, or NRE 438 (or ENVIRON 438).

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 analysis-computer package. Course evaluation is based on a combination of semester examinations, a final examination, weekly homework, and lab participation.

STATS 350 — Introduction to Statistics and Data Analysis
Section 002, LEC

FA 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, or NRE 438 (or ENVIRON 438).

A one term course in applied statistical methodology from an analysis-of-data 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 350 — Introduction to Statistics and Data Analysis
Section 003, LEC

Instructor: Venable Jr,Thomas Calvin

FA 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, or NRE 438 (or ENVIRON 438).

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 analysis-computer package. Course evaluation is based on a combination of semester examinations, a final examination, weekly homework, and lab participation.

STATS 350 — Introduction to Statistics and Data Analysis
Section 004, LEC

Instructor: Gunderson,Brenda K

FA 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, or NRE 438 (or ENVIRON 438).

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 analysis-computer package. Course evaluation is based on a combination of semester examinations, a final examination, weekly homework, and lab participation.

STATS 350 — Introduction to Statistics and Data Analysis
Section 005, LEC

Instructor: Gunderson,Brenda K

FA 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, or NRE 438 (or ENVIRON 438).

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 analysis-computer package. Course evaluation is based on a combination of semester examinations, a final examination, weekly homework, and lab participation.

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

FA 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, or NRE 438 (or ENVIRON 438).

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 analysis-computer package. Course evaluation is based on a combination of semester examinations, a final examination, weekly homework, and lab participation.

STATS 400 — Applied Statistical Methods
Section 001, LEC

Instructor: Hansen,Bendek B

FA 2007
Credits: 4
Reqs: BS, QR/1

Credit Exclusions: No credit granted to those who have completed or are enrolled in ECON 404 or 405, or STATS 265, 350, 405, or 412, or IOE 265, or NRE 438 (or ENVIRON 438).

Statistics and the scientific method; observational study versus designed experiment; visualization; introduction to probability; statistical inference; confidence intervals; one-sample tests of hypothesis; two-sample problems; analysis of variance (ANOVA); blocked designs; tests for association and independence (chi-square tests); regression and correlation; and non-parametric tests. Course format includes lectures (3 hours per week) and a laboratory (1.5 hours per week).

Advisory Prerequisite: High School Algebra.

STATS 401 — Applied Statistical Methods II
Section 001, LEC

FA 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, two-sample problems, one-way analysis of variance; multiple linear regression, diagnostics and model selection; two-way analysis of variance, multiple comparisons, and other selected topics. The only prerequisites are STATS 350 (or 400) and MATH 115.

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 405 — Introduction to Statistics
Section 001, LEC

Instructor: Davis,Lucas William

FA 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.

This course is designed for economics concentrators but is sufficiently general to serve non-economics concentrators as well. The emphasis is on understanding rather than on "cookbook" applications. Students are expected to know basic algebra and basic calculus. Since the course emphasizes the foundations of statistical inference, it is recommended that after finishing the course students elect ECON 406 or a similar course in the Statistics department to gain experience with applications and computational methods.

This course is designed for quantitatively oriented students who are comfortable with abstract concepts and mathematical techniques. Students who prefer a broader, less rigorous approach to statistics should elect ECON 404. Evaluation of students in the course is based on examinations and homework assignments. There are three hours of lectures and one hour of discussion per week. ECON 405 is a prerequisite for ECON 406 (Econometrics).

Principles of statistical inference, including: probability, experimental and theoretic derivation of sampling distributions, hypothesis testing, estimation, and simple regression.

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 406 — Introduction to Statistical Computing
Section 001, LEC

Instructor: Shedden,Kerby A

FA 2007
Credits: 4
Reqs: BS

Acquaints students with selected topics in statistical computing, including basic numerical aspects, iterative statistical methods, principles of graphical analyses, simulation and Monte Carlo methods, generation of random variables, stochastic modeling, importance sampling, and numerical and Monte Carlo integration. Three hours of lecture and one and one-half hour laboratory session each week.

Enforced Prerequisites: (STATS 401 or 412 or 425) or MATH 425

STATS 412 — Introduction to Probability and Statistics
Section 001, LEC

Instructor: Levina,Elizaveta; homepage

FA 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

FA 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 003, LEC

FA 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

FA 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: Woodroofe,Michael B; homepage

FA 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

FA 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: Woodroofe,Michael B; homepage

FA 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

FA 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

FA 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: Atchade,Yves A

FA 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: Atchade,Yves A

FA 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 009, LEC

FA 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: Keener,Robert W

FA 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 Chi-squared, 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
Chi-Squared 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 426 — Introduction to Theoretical Statistics
Section 002, LEC

FA 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 Statistics 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 Chi-squared, 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
Chi-Squared 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 466 — Statistical Quality Control
Section 001, LEC

Instructor: Shi,Jianjun

FA 2007
Credits: 3

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; and charting, smoothing, forecasting, and prediction of discrete time series.

Enforced Prerequisites: STATS 265 and 401 or IOE 366, with at least a C-; or graduate standing.

STATS 470 — Introduction to the Design of Experiments
Section 001, LEC

FA 2007
Credits: 4
Reqs: BS

This course will introduce students to basic principles in classical experimental design, including randomization, replication, confounding, interaction, covariates, and 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, and optimal. There will be regular assignments and a final exam. Class format is 3 hours of lecture and 1.5 hours of laboratory per week.

STATS 489 — Independent Study in Statistics
Section 001, IND

FA 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

FA 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

FA 2007
Credits: 3
Reqs: BS

Course outline:

Linear Regression Models: definition, fitting, Gauss-Markov theorem, inference, interpretation of results, meaning of regression coefficients, identifiability, diagnostics, influential observations, multicollinearity, lack of fit, 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 make-up exam.

Advisory Prerequisite: STATS,MATH 417, and STATS 350 or 426.

STATS 503 — Applied Multivariate Analysis
Section 001, LEC

FA 2007
Credits: 3
Reqs: BS

Topics in applied multivariate analysis including Hotelling's T-squared, 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 504 — Statistical Consulting
Section 001, LEC

Instructor: Rothman,Edward D

FA 2007
Credits: 3
Reqs: BS

Applications of statistics to problems in engineering, physical and social sciences; students will be expected to analyze data and write reports.

Advisory Prerequisite: STATS. 401 or 500.

STATS 505 — Econometric Analysis I
Section 001, LEC

Instructor: Kilian,Lutz; homepage

FA 2007
Credits: 3
Reqs: BS

ECON 671 and 672 form the basic required sequence in econometrics for all doctoral students. Their purpose is to provide Ph.D. students with the training needed to do the basic quantitative analysis generally understood to be part of the background of all modern economists. This includes: the theory and practice of testing hypotheses, statistical estimation theory, the basic statistical theory underlying the linear model, an introduction to econometric methods, and the nature of the difficulties which arise in applying statistical procedures to economic research problems.

Advisory Prerequisite: Graduate standing and permission of instructor.

STATS 508 — Statistical Analysis of Financial Data
Section 001, LEC

Instructor: Thelen,Brian J; homepage

FA 2007
Credits: 3
Reqs: BS

This course will cover basic topics involved in modeling and analysis of financial data. These include linear and non-linear regression, nonparametric and semi-parametric regression, selected topics on the analysis of multivariate data and dimension-reduction, and time series analysis. Examples and data from financial applications will be used to motivate and illustrate the methods.

Advisory Prerequisite: MATH 417, STATS 425, 426 or equivalent

STATS 520 — Mathematical Methods in Statistics
Section 001, LEC

Instructor: Stoev,Stilian Atanasov

FA 2007
Credits: 3
Reqs: BS

This course provides the mathematical background for graduate courses in statistics and probability. The course reviews basic notions from matrix algebra and real analysis. It then introduces students to measure theory and integration. In particular, the content covers definition of measures and measurable functions, convergence theorems, Legesque integration, Lp spaces, signed measures, Radon-Nikodym theorem, integration on product spaces.

Advisory Prerequisite: MATH 451/EQ.

STATS 525 — Probability Theory
Section 001, LEC

Instructor: Egami,Masahiko; homepage

FA 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 610-611 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

FA 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 535 — Reliability
Section 001, LEC

Instructor: Nair,Vijayan N; homepage

FA 2007
Credits: 3

This course will cover 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 Concepts in Reliability

— Component and System Reliability

— Aging

— Hazard Rates and Failure Rates

  • Component Reliability Modeling and Inference

    — Common Models for Component Reliability

    — Analysis of Time-to-Failure Data

* Types of Censoring Schemes

* Nonparametric Techniques

* Graphical and Formal Goodness-of-Fit Tests for Model Selection

* Parametric Techniques

    — Analysis of Degradation Data

  • Reliability, Availability, and Maintainability for Repairable Systems

    — System Structures

    — Common Models for System Reliability

    — Analysis of Time-Between-Failure Data

    — Maintenance and Availability

  • Accelerated Stress Testing for Reliability Assessment
  • Reliability Improvement Through Experimental Design
  • Special Topics: Warranty Data Analysis, Stress-Strength Models, etc.

TEXT: Statistical Methods for Reliability Data by Meeker and Escobar (1998), Wiley.

This will be supplemented by selected material from engineering text books in reliability.

Advisory Prerequisite: STATS 425 and 426 (or IOE 316 and 366).

STATS 576 — Advanced Econometrics II
Section 001, LEC

Instructor: Ng,Serena; homepage

FA 2007
Credits: 3
Reqs: BS

Generalized least squares, multivariate multiple regression, simultaneous equation models (including problems of identification, estimation by equation and system methods, and forecasting), introduction to asymptotic theory, and estimation problems in time series models.

Advisory Prerequisite: ECON 678/STATS 575 or equivalent.

STATS 580 — Methods and Theory of Sample Design
Section 001, LEC

Instructor: Heeringa,Steven G

FA 2007
Credits: 3
Reqs: BS

This course is concerned with the theory underlying methods of survey sampling widely used in practice. It covers basic techniques of simple random sampling, stratification, systematic sampling, cluster and multi-stage sampling, and probability proportional to size sampling. It also examines methods of variance estimation for complex sample designs, including Taylor series expansions, balanced repeated replications, and jackknife methods. It covers specialized topics, including stratification and subclasses, multi-phase or double sampling, ratio estimation, selection with unequal probabilities without replacement, non-response adjustments, imputation, and small area estimation. The course examines both practical applications of sampling techniques as well as the theory supporting the methods.

Advisory Prerequisite: Three or more courses in statistics and preferably a course in methods of survey sampling.

STATS 600 — Linear Models
Section 001, LEC

Instructor: Shedden,Kerby A

FA 2007
Credits: 3

This is an advanced introduction to regression modeling and prediction, including traditional and modern computationally-intensive methods. The following topics will be covered:  1) Theory and practice of linear models, including the relevant distribution theory, estimation, confidence and prediction intervals, testing, models and variable selection generalized least squares, robust fitting, and diagnostics; 2) Generalized linear models, including likelihood formulation, estimation and inference, diagnostics, and analysis of deviance; and 3) Large and small-sample inference as well as inference via the bootstrap, cross-validation, and permutation tests.

Advisory Prerequisite: Knowledge of linear algebra; STATS 425 and STATS 426 or equivalent courses in probability and statistics

STATS 610 — Statistical Inference
Section 001, LEC

Instructor: Keener,Robert W

FA 2007
Credits: 3

Review of probability, exponential families, sufficiency, completeness, Basu's Theorem, unbiased estimation, curved exponential families, information inequalities, conditional probability, Bayesian estimation, large sample theory.

Advisory Prerequisite: STATS,MATH 451, STATS 425, and 426 or equivalent courses in probability, statistics and real analysis

STATS 620 — Applied Probability and Stochastic Modeling
Section 001, LEC

Instructor: Ionides,Edward L; homepage

FA 2007
Credits: 3

Basics of probability at an advanced level. Specific topics include: discrete probability spaces, the weak law of large numbers, the de Moivre-Laplace theorems, classes of sets, algebras, measures, extension of measures, countable additivity and Lebesgue and product measures. Also: measurable functions, random variables, conditional probability, independence, the Borel-Cantelli lemmas and the zero-one law. The course will additionally cover: integration, convergence theorems, inequalities, Fubini's theorem, the Radon-Nikodym theorem, distribution functions, expectations, and the strong law of large numbers.

Advisory Prerequisite: STATS,MATH 451, STATS 425, and 426 or equivalent courses in probability, statistics and real analysis

STATS 625 — Probability and Random Processes I
Section 001, LEC

Instructor: Bayraktar,Erhan; homepage

FA 2007
Credits: 3
Reqs: BS

Axiomatics; measures and integration in abstract spaces. Fourier analysis, characteristic functions. Conditional expectation, Kolmogoroff extension theorem. Stochastic processes; Wiener-Levy, infinitely divisible, stable. Limit theorems, law of the iterated logarithm.

Advisory Prerequisite: MATH 597 and Graduate standing.

STATS 626 — Probability and Random Processes II
Section 001, LEC

FA 2007
Credits: 3
Reqs: BS

Selected topics from among: diffusion theory and partial differential equations; spectral analysis; stationary processes, and ergodic theory; information theory; martingales and gambling systems; theory of partial sums.

Advisory Prerequisite: MATH 625/STATS 625 and Graduate standing.

STATS 808 — Seminar in Applied Statistics I
Section 001, SEM

Instructor: Banerjee,Moulinath

FA 2007
Credits: 1

A seminar on topics in applied statistics. Content varies by term and instructor.

Advisory Prerequisite: Graduate standing.

STATS 810 — Literature Proseminar I
Section 001, LEC

Instructor: Shedden,Kerby A

FA 2007
Credits: 2

This course is designed to acquaint students with classical papers in mathematics 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 990 — Dissertation/Precandidate
Section 001, IND

FA 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

FA 2007
Credits: 1

A seminar for all beginning graduate student instructors, consisting of a two-day 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

FA 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

 
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