# Courses in Statistics (Division 489)

## Fall Term, 1999 (September 8 – December 22, 1999)

Take me to the Fall Term '99 Time Schedule for Statistics.

### Stat. 100. Introduction to Statistical Reasoning.

#### Instructor(s): Biswas

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Soc. 210, Stat. 265, 311, 402, 405, or 412, or Econ. 404 or 405. (4). (MSA). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/100F99/

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, in-class quizzes, weekly homework, and lab participation. The course format includes three lectures (3 hours per week) and a laboratory (1 hour per week).

### Stat. 100. Introduction to Statistical Reasoning.

#### Instructor(s): Dass

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Soc. 210, Stat. 265, 311, 402, 405, or 412, or Econ. 404 or 405. (4). (MSA). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/100F99/

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, in-class quizzes, weekly homework, and lab participation. The course format includes three lectures (3 hours per week) and a laboratory (1 hour per week).

### Stat. 100. Introduction to Statistical Reasoning.

#### Instructor(s): Brenda Gunderson (bkg@umich.edu)

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Soc. 210, Stat. 265, 311, 402, 405, or 412, or Econ. 404 or 405. (4). (MSA). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/100F99/

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, in-class quizzes, weekly homework, and lab participation. The course format includes three lectures (3 hours per week) and a laboratory (1 hour per week).

### Stat. 100. Introduction to Statistical Reasoning.

#### Instructor(s): Martha Aliaga (aliaga@umich.edu)

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Soc. 210, Stat. 265, 311, 402, 405, or 412, or Econ. 404 or 405. (4). (MSA). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/100F99/

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, in-class quizzes, weekly homework, and lab participation. The course format includes three lectures (3 hours per week) and a laboratory (1 hour per week).

### Stat. 265/IOE 265. Probability and Statistics for Engineers.

#### Instructor(s): Karl Majeske (kdm@umich.edu)

Prerequisites & Distribution: Math. 116 and Engin. 101. No credit granted to those who have completed or are enrolled in Stat. 311, 405, or 412, or Econ. 405. (4). (Excl). (BS). CAEN lab access fee required for non-Engineering students.

Credits: (4).

Lab Fee: CAEN lab access fee required for non-Engineering students.

Course Homepage: http://www.engin.umich.edu/class/ioe265/

Graphical representation of Data; axioms of Probability; conditioning, Bayas Theorem; discreet distributions (Geometric, Binomial, Poisson); continuous distributions (normal exponential, Weibull), point and interval estimation, likelihood functions, test of hypotheses for Means, Variances, and Proportions for one and two populations.

Three hours of lecture and one hour of lab each week. Regular homework and a final exam.

 Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

### Stat. 402. Introduction to Statistics and Data Analysis.

#### Instructor(s): Mielniczuk

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 405, or 412. (4). (NS). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/402F99/

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 402 has no prerequisite and has been elected by students whose mathematics background includes only high school algebra. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods. The course format includes lectures (3 hours per week) and a laboratory (l.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a statistical analysis-computer package. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, weekly homework, and lab participation, statistical analysis. Include: Term exams on Wednesday, October 13, 6-8 P.M. and Wednesday November 17, 6-8 P.M.

### Stat. 402. Introduction to Statistics and Data Analysis.

#### Instructor(s): Ed Rothman (erothman@umich.edu)

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 405, or 412. (4). (NS). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/402F99/

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 402 has no prerequisite and has been elected by students whose mathematics background includes only high school algebra. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods. The course format includes lectures (3 hours per week) and a laboratory (l.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a statistical analysis-computer package. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, weekly homework, and lab participation, statistical analysis. Include: Term exams on Wednesday, October 13, 6-8 P.M. and Wednesday November 17, 6-8 P.M.

### Stat. 402. Introduction to Statistics and Data Analysis.

#### Instructor(s): Biswas

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 405, or 412. (4). (NS). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/402F99/

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 402 has no prerequisite and has been elected by students whose mathematics background includes only high school algebra. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods. The course format includes lectures (3 hours per week) and a laboratory (l.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a statistical analysis-computer package. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, weekly homework, and lab participation, statistical analysis. Include: Term exams on Wednesday, October 13, 6-8 P.M. and Wednesday November 17, 6-8 P.M.

### Stat. 402. Introduction to Statistics and Data Analysis.

#### Instructor(s): Brenda Gunderson (bkg@umich.edu)

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 405, or 412. (4). (NS). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/402F99/

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 402 has no prerequisite and has been elected by students whose mathematics background includes only high school algebra. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods. The course format includes lectures (3 hours per week) and a laboratory (l.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a statistical analysis-computer package. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, weekly homework, and lab participation, statistical analysis. Include: Term exams on Wednesday, October 13, 6-8 P.M. and Wednesday November 17, 6-8 P.M.

### Stat. 402. Introduction to Statistics and Data Analysis.

#### Instructor(s): Gore

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 405, or 412. (4). (NS). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/402F99/

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 402 has no prerequisite and has been elected by students whose mathematics background includes only high school algebra. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods. The course format includes lectures (3 hours per week) and a laboratory (l.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a statistical analysis-computer package. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, weekly homework, and lab participation, statistical analysis. Include: Term exams on Wednesday, October 13, 6-8 P.M. and Wednesday November 17, 6-8 P.M.

### Stat. 402. Introduction to Statistics and Data Analysis.

#### Section 006 – Evening Exams: Wednesday, October 13 and Wednesday, November 17, from 6-8 pm.

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 405, or 412. (4). (NS). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/402F99/

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 402 has no prerequisite and has been elected by students whose mathematics background includes only high school algebra. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods. The course format includes lectures (3 hours per week) and a laboratory (l.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a statistical analysis-computer package. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, weekly homework, and lab participation, statistical analysis. Include: Term exams on Wednesday, October 13, 6-8 P.M. and Wednesday November 17, 6-8 P.M.

### Stat. 402. Introduction to Statistics and Data Analysis.

#### Instructor(s): Bingham

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 405, or 412. (4). (NS). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/402F99/

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 402 has no prerequisite and has been elected by students whose mathematics background includes only high school algebra. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods. The course format includes lectures (3 hours per week) and a laboratory (l.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a statistical analysis-computer package. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, weekly homework, and lab participation, statistical analysis. Include: Term exams on Wednesday, October 13, 6-8 P.M. and Wednesday November 17, 6-8 P.M.

### Stat. 405/Econ. 405. Introduction to Statistics.

#### Instructor(s): Lutz Kilian (lkilian@umich.edu)

Prerequisites & Distribution: Math. 116 or 118. Juniors and seniors may elect this course concurrently with Econ. 101 or 102. No credit granted to those who have completed or are enrolled in Stat. 265, 311 or 412. Students with credit for Econ. 404 can only elect Stat. 405 for 2 credits and must have permission of instructor. (4). (MSA). (BS). (QR/1).

Credits: (4).

Course Homepage: No Homepage Submitted.

See Economics 405.001.

### Stat. 406. Introduction to Statistical Computing.

#### Instructor(s): Kerby Shedden (kshedden@umich.edu)

Prerequisites & Distribution: Stat. 425 and 402. (4). (Excl). (BS).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~kshedden/Courses/Stat406/

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, numerical and Monte Carlo integration. Three hours of lecture and one and one-half hour laboratory session each week.

 Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

### Stat. 412. Introduction to Probability and Statistics.

#### Instructor(s): Edsel Pena (epena@umich.edu)

Prerequisites & Distribution: Prior or concurrent enrollment in Math. 215 and CS 183. No credit granted to those who have completed or are enrolled in Econ. 405, or Stat. 265, 311, or 405. One credit granted to those who have completed Stat. 402. (3). (MSA). (BS).

Credits: (3).

Course Homepage: http://www-personal.umich.edu/~epena/stat412.html

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 analysis of variance. Examples will emphasize applications to the natural sciences and engineering. There will be regular homework, and a final exam. The grades will be distributed according to the following weights. Homeworks = 25%; First Exam = 25%; Second Exam = 25%; Final Exam = 25%. The homeworks will be assigned each week and their solutions will be due on the following week. Course Text: Devore, Jay (1995). Probability and Statistics for Engineering and the Sciences. 4th edition, Duxbury Press: Pacific Grove.

### Stat. 413. The General Linear Model and Its Applications.

#### Section 001.

Prerequisites & Distribution: Stat. 402 and Math. 217; concurrent enrollment in Stat. 425. Students who have not taken Math. 217 should elect Stat. 403. Two credits granted to those who have completed Stat. 403. (4). (Excl). (BS).

Credits: (4).

Course Homepage: No Homepage Submitted.

This course will introduce students to the general linear model and its assumptions, and will cover topics such as the geometry of the model projections, least squares estimation, residuals, normal distribution theory results, inference on parameters, diagnostic tools, and applications in analysis of variance, design, and the series. Three hours of lecture and 1.5 hours of lab per week. Regular homework and a final exam.

### Stat. 425/Math. 425. Introduction to Probability.

#### Instructor(s): Jerome Wolbert (wolbert@umich.edu)

Prerequisites & Distribution: Math. 215, 255, or 285. (3). (MSA). (BS).

Credits: (3).

Course Homepage: http://www.math.lsa.umich.edu/~wolbert/425/

See Mathematics 425.001.

 Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: 2

### Stat. 425/Math. 425. Introduction to Probability.

#### Instructor(s): Andrius Jankunas

Prerequisites & Distribution: Math. 215, 255, or 285. (3). (MSA). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

Sample spaces and axiomatic probability; elementary combinatorics; conditional probability and independence; random variables; probability distributions, including binomial, Poisson, Gamma, and normal; expectation, mean and variance; moment generating functions; the law of large numbers; central limit theorem.

### Stat. 425/Math. 425. Introduction to Probability.

#### Instructor(s): Mielniczuk

Prerequisites & Distribution: Math. 215, 255, or 285. (3). (MSA). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

Sample spaces and axiomatic probability; elementary combinatorics; conditional probability and independence; random variables; probability distributions, including binomial, Poisson, Gamma, and normal; expectation, mean and variance; moment generating functions; the law of large numbers; central limit theorem.

### Stat. 425/Math. 425. Introduction to Probability.

#### Instructor(s): P Jeganathan (jegan@umich.edu)

Prerequisites & Distribution: Math. 215, 255, or 285. (3). (MSA). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

Sample spaces and axiomatic probability; elementary combinatorics; conditional probability and independence; random variables; probability distributions, including binomial, Poisson, Gamma, and normal; expectation, mean and variance; moment generating functions; the law of large numbers; central limit theorem.

### Stat. 425/Math. 425. Introduction to Probability.

#### Section 006.

Prerequisites & Distribution: Math. 215, 255, or 285. (3). (MSA). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

See Mathematics 425.001.

 Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: 2

### Stat. 426. Introduction to Mathematical Statistics.

#### Section 001.

Prerequisites & Distribution: Stat. 425. (3). (MSA). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

This course covers the basic ideas of statistical inference, including sampling distributions, estimation, confidence intervals, hypothesis testing, regression, analysis of variance, nonparametric testing, and Bayesian inference. The sequence of Statistics 425/426 serves as a prerequisite for more advanced Statistics courses, regular homework and a final exam.

### Stat. 466/IOE 466/Manufacturing 466. Statistical Quality Control.

#### Instructor(s): Jianjun Shi (shihang@umich.edu)

Prerequisites & Distribution: Stat. 265 or 311. (3). (Excl). (BS). CAEN lab access fee required for non-Engineering students.

Credits: (3).

Lab Fee: CAEN lab access fee required for non-Engineering students.

Course Homepage: No Homepage Submitted.

Design and analysis of procedures for forecasting and control of production processes. Topics include: attribute and variables sampling plans; sequential sampling plans; rectifying control procedures; charting, smoothing, forecasting, and prediction of discrete time series.

### Stat. 470. Experimental Design.

#### Section 001.

Prerequisites & Distribution: Stat. 402. (4). (Excl). (BS).

Credits: (4).

Course Homepage: No Homepage Submitted.

This course will introduce students to basic principles in classical experimental design, including randomization, replication, confounding, interaction, covariates, use of the general linear model. Students will be introduced to the following designs: completely randomized, randomized blocks, Latin squares, incomplete blocks, factorial, split plot, Taguchi, response surface, optimal. There will be regular assignments and a final exam. Class format is 3 hours of lecture and 1.5 hours of laboratory per week.

### Stat. 499. Honors Seminar.

Prerequisites & Distribution: Permission of departmental Honors advisor. (2-3). (Excl). (INDEPENDENT).

Credits: (2-3).

Course Homepage: No Homepage Submitted.

### Stat. 500. Applied Statistics I.

#### Instructor(s): Julian Faraway (faraway@umich.edu)

Prerequisites & Distribution: Math. 417, and Stat. 402 or 426. (3). (Excl). (BS).

Credits: (3).

Course Homepage: http://www.stat.lsa.umich.edu/~faraway/stat500/

Linear Models: Definition, fitting, identifiability, multicollinearity, Gauss-Markov theorem, variable selection, diagnostics, transformations, influential observations, robust procedures, ANOVA and analysis of covariance, interpretation of results, meaning of regression coefficients. Randomised block, factorial designs. Random effects experiments.

### Stat. 503. Applied Multivariate Analysis.

#### Instructor(s): George Michailidis (gmichail@umich.edu)

Prerequisites & Distribution: Stat. 500. (3). (Excl). (BS).

Credits: (3).

Course Homepage: http://www.stat.lsa.umich.edu/~gmichail/stat503-f99/

Topics in applied multivariate analysis including Hotelling's T2 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 a computer will be stressed.

### Stat. 505/Econ. 673. Econometric Analysis.

#### Instructor(s): Paul Rilstone

Prerequisites & Distribution: Permission of instructor. (3). (Excl). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

This course is designed for first-year graduate students in economics, business, and related subjects. It involves a fairly rigorous development of statistical reasoning and methods relating to hypothesis testing, estimation, and regression analysis. Students are supposed to have had a course in calculus and in introductory statistics. Knowledge of matrix algebra is required. Evaluation of students is based on midterm and final examinations and weekly assignments. Students taking this course are expected to take Economics 674 – Econometric Analysis II in the following term.

### Stat. 510. Mathematical Statistics I.

#### Section 001.

Prerequisites & Distribution: Math. 450 or 451, and a course in probability or statistics. (3). (Excl). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

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

### Stat. 525/Math. 525. Probability Theory.

#### Section 001.

Prerequisites & Distribution: Math. 450 or 451. Students with credit for Math. 425/Stat. 425 can elect Math. 525/Stat. 525 for only one credit. (3). (Excl). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

See Mathematics 525.001.

 Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: 2

### Stat. 535/IOE 562. Reliability.

#### Section 001.

Prerequisites & Distribution: Stat. 425 and 426 (or IOE 316 and 366). (3). (Excl). (BS). CAEN lab access fee required for non-Engineering students.

Credits: (3).

Lab Fee: CAEN lab access fee required for non-Engineering students.

Course Homepage: No Homepage Submitted.

This course will cover the important reliability concepts and methodology that arise in modeling, assessing and improving product reliability and in analyzing field and warranty data. Topics will be selected from the following: Basic reliability concepts; Common parametric models for component reliability; Censoring schemes; Analysis of time-to-failure data; Accelerated testing for reliability assessment; Modeling and analyzing repairable systems reliability; Analysis of warranty and field-failure data; Maintenance policies and availability; Reliability improvement through experimentation.

#### Instructor(s): Stephen Pollock (pollock@umich.edu)

Prerequisites & Distribution: Stat. 425. (3). (Excl). (BS). CAEN lab access fee required for non-Engineering students.

Credits: (3).

Lab Fee: CAEN lab access fee required for non-Engineering students.

Course Homepage: No Homepage Submitted.

Axiomatic foundations for, and assessment of, probability and utility; formulation of decision problems; risk functions, admissibility; likelihood functions and the likelihood principle; natural conjugate a priori distributions; Bayesian regresion analysis and hypothesis testing; hierarchical models; credible intervals; numerical analysis; applications to decision-making.

### Stat. 575/Econ. 775. Econometric Theory I.

#### Instructor(s): Paul Rilstone

Prerequisites & Distribution: Math. 417 and 425 or Econ. 653, 654, 673, and 674. (3). (Excl). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

The purpose of this course is to develop the results of asymptotic distribution theory needed for statistical inference in econometrics and to use these results to derive the properties of various estimators and test procedures used in econometrics. The course is a prerequisite for Statistics 576 (Econometric Theory II).