STAA Course Descriptions


These courses will be offered beginning in the 2012-2013 academic year. Registration for STAA courses will be limited to our MAS students and people who have been given specific permission to take these courses. All courses are offered based on availability and student interest. Method of lecture delivery is streamed video. DVDs or download access may be available for some courses. Textbook and software requirements for these courses will be posted as they become available. For additional details regarding STAA courses, please contact Dr. Anderson. The planned schedule of online STAA course offerings is posted here. STAA and STAT course offerings for the next few terms can be found here.

Notes:

  1. We enforce our prerequisites! If you cannot show that you have taken the prerequisites for a course, you may be dropped from the class roster. Please make sure that you have the indicated prerequisites before registering!
  2. Unless otherwise indicated, all STAA courses use SAS and R statistical software. Specific requirements for using SAS can be found here.
  3. Classes with proctored exams generally give a 4-6 day window (including a weekend) in which students must complete exams.
  4. For homework submission, students must have access to a fax or scanner.


STAA551: Regression Models and Applications 02 Credits.

Prerequisite: Admission to the MAS program or written consent of instructor. (A background including CSSA and MSSA is assumed.)

Estimation and hypothesis testing methods in linear models including t-tests, ANOVA, regression, including multiple regression. Residual analyses, transformations, goodness of fit, interaction and confounding.

Textbook: Applied Regression Analysis and Other Multivariable Methods 5th Edition, by David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg ISBN-13: 978-1285051086 ISBN-10: 1285051084

Computer Software: R.

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on bi-weekly homework sets, two proctored mid-term exams, and a proctored final exam.

Credit allowed for only one course: STAT540, STAT512, STAA551.

Note: This course is approved by the Society of Actuaries for the regression component of the Validation by Educational Experience (VEE).

To register for this course, click here



STAA552: Generalized Regression Models 02 Credits.

Co-requisite: STAA551 and STAA561. (A background including CSSA and MSSA is assumed.)

Categorical data analysis, estimation and testing for contingency tables, introduction to generalized linear models, logit and probit models for binary regression, extensions to nominal and ordinal multicategory responses, count data, Poisson and negative binomial regression, log-linear models.  

Textbook: Categorical Data Analysis, 3rd ed., by Alan Agresti (Wiley Series in Probability and Statistics 978-0-470-46363-5). Note: This is not the same text as An Introduction to Categorical Data Analysis, also by Alan Agresti!

Computer Software: R

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on six homework sets, two proctored quizzes and a proctored final exam.

Credit allowed for only one course: STAT645, STAA552.

To register for this course, click here



STAA553: Experimental Design 02 Credits.

Prerequisite: STAA551 and STAA562. (A background including CSSA and MSSA is assumed.)

Analysis of variance, covariance, randomized block, latin square, factorial, balanced and unbalanced designs. Applications to agriculture, biosciences. Implementation in SAS and R

Textbook: Textbook not required. Please refer to syllabus on Canvas before purchasing text.

Computer Software: R and SAS

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on weekly homework sets, a proctored mid-term exam, and a proctored, cumulative final exam.

Credit allowed for only one of: STAT650 or [STAA553 and STAA554]

To register for this course, click here



STAA554: Mixed Models 02 Credits.

Co-requisite: STAA552 and STAA553. (A background including CSSA and MSSA is assumed.)

Topics in linear mixed models with fixed and random predictors, balanced and unbalanced cases. Statistical topics will be integrated with the use of the computer packages SAS and R. Non-linear and generalized linear models are secondary topics.

Textbook: No textbook required.

Computer Software: R and SAS.

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on weekly homework sets, a proctored mid-term exam, and a proctored, cumulative final exam.

Credit allowed for only one of: STAT650 or [STAA553 and STAA554].

To register for this course, click here



STAA556: Statistical Consulting 03 Credits.

Prerequisite: 28 credits of STAA coursework, or one year in the MS or PhD program.

Consultant-client interactions, communications, ethical practices. Complete a consulting project and provide a report.

Textbook: No textbook required.

Proctor: This course does not require an exam proctor.

Grading: Course grade is based on class assignments and a formal consulting project.

Credit allowed for only one course: STAT586, STAA556.

To register for this course, click here



STAA561: Probability with Applications 02 Credits.

Prerequisite: Admission to the MAS program or written consent of instructor. (A background including CSSA and MSSA is assumed.)

Random variables, continuous and discrete distributions, expectations, joint and conditional distributions, transformations. Applications to capture/recapture, financial and industrial models.

Textbook: No textbook required.

Computer Software: R

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on weekly homework sets, three proctored (bi-weekly) quizzes, and a proctored cumulative final exam.

Credit allowed for only one course: STAT520, STAA561

To register for this course, click here



STAA562: Mathematical Statistics with Applications 02 Credits.

Co-requisite: STAA561. (A background including CSSA and MSSA is assumed.)

Theory and applications of estimation, testing, and confidence intervals. Computer simulations; sampling from the normal distribution.

Textbook: No textbook required.

Computer Software: R

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on weekly homework sets, three proctored (bi-weekly) quizzes, and a proctored cumulative final exam.

Credit allowed for only one course: STAT530, STAA562.

To register for this course, click here



STAA565: Quantitative Reasoning 01 Credit.

Co-requisite: STAA551

Confounding, types of bias such as selection bias and regression effect bias, Simpson’s paradox, experiments versus observational studies, etc.

Textbook: No textbook required.

Computer Software: R

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on weekly homework sets and three proctored quizzes.

To register for this course, click here



STAA566: Computational and Graphical Methods 01 Credit.

Prerequisite: Admission to the MAS program or written consent of instructor. (A background including CSSA is assumed.)

Exploratory data analysis using graphics, effective communication with graphs, data reduction methods.

Textbook: No textbook required.

Computer Software: R

Proctor: This course does not require an exam proctor.

Grading: Course grade is based on bi-weekly homework sets.

Credit allowed for only of: STAT600 or [STAA566 and STAA567].

To register for this course, click here



STAA567: Computational and Simulation Methods 01 Credit.

Co-requisite: STAA551 and STAA561. (A background including CSSA and MSSA is assumed.)

Sampling methods, simulating distributions of test statistics, optimization.

Textbook: There is no textbook required for this course, however students may want to use as a reference:Computational Statistics, G. H. Givens and J. A. Hoeting, Wiley (available online at lib.colostate.edu).  ISBN 0470533315

Computer Software: R

Proctor: This course does not require an exam proctor.

Grading: Course grade is based on homework assignments.

Credit allowed for only one of: STAT600 or [STAA566 and STAA567].

To register for this course, click here



STAA568: Topics Industrial/Organizational Statistics 01 Credit.

Co-requisite: STAA553 and STAA561 or written consent of instructor.

This course will focus on a quality improvement methodology called Lean Six Sigma.  The Six Sigma roadmap – DMAIC – and Lean concepts will be covered and applied to a hands on project that students will select and execute during the class.  The best way to learn this approach is by applying the tools to real life projects.  We will conduct class on two Saturdays, with two weeks between classes.

Textbooks:

Computer Software: The course will cover use of Minitab software.  Minitab 17 Student Version can be purchased here. Minitab 16 may be used if you already have it. This software is not required, but it is in widespread use and includes routines in the software that facilitate using the DMAIC tools.

Proctor: This course does not require an exam proctor.

Grading: Course grade is based on a course project.

Credit allowed for only one course: STAT547, STAA568.

To register for this course, click here



STAA571: Survey Statistics 02 Credits.

Prerequisite: STAA551 and STAA562. (A background including CSSA and MSSA is assumed.)

Estimation and variance estimation for complex survey designs, accounting for stratification, clustering, and unequal probabilities of selection.  Taught jointly with STAT605 in alternate years. 

Textbook: Practical Tools for Designing and Weighting Survey Samples (Statistics for Social and Behavioral Sciences) 2013 edition by Richard Valliant, Jill A. Dever, and Frauke Kreuter ISBN-13: 978-1461464488

Computer Software: R (required).

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on homework sets, a proctored mid-term exam, and a proctored cumulative final exam.

Credit allowed for only one course: STAT605, STAA571.

To register for this course, click here



STAA572: Nonparametric Methods 02 Credits.

Co-requisite: STAA551 and STAA561. (A background including CSSA and MSSA is assumed.)

Rank-based methods, nonparametric inferential techniques, scatterplot smoothing, nonparametric function estimation, environmental applications.

Textbook: Introduction to Modern Nonparametric Statistics, by James J. Higgins (Cengage ISBN:9780534387754)

Computer Software: R

Proctor: This course does not require an exam proctor.

Grading: Course grade is based on homework sets, two mid-term exams, and a final exam.

Credit allowed for only one course: STAT570, STAA572.

To register for this course, click here



STAA573: Analysis of Time Series 02 Credits.

Co-requisite: STAA551 and STAA561. (A background including CSSA and MSSA is assumed.)

Moving average and auto-regressive correlation structures, estimation and forecasting, modeling seasonality. Financial and environmental applications.

Textbook: Time Series Analysis and Its Applications: With R Examples, 3rd ed., by Shumway & Stoffer.

Computer Software: R

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on homework sets, a proctored mid-term exam, and a proctored cumulative final exam.

Credit allowed for only one course: STAT525, STAA573.

Note: This course is approved by the Society of Actuaries for the time series component of the Validation by Educational Experience (VEE).

To register for this course, click here



STAA574: Methods in Multivariate Analysis 02 Credits.

Prerequisite: STAA 551 and STAA561. Note: R programming skills (CSSA) are expected, as well as strong knowledge of linear algebra (MSSA).

Multivariate ANOVA, principal components, factor analysis, cluster analysis, discrimination analysis.

Textbooks (optional): An R and S-Plus® Companion to Multivariate Analysis, by Brian Everitt (available for free on Springer) and

Applied Multivariate Statistical Analysis, 6th ed., by Johnson & Wichern ISBN 9780131877153.

Computer Software: R

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on homework sets, a proctored mid-term exam, and a proctored cumulative final exam.

Credit allowed for only one course: STAT560, STAT460, STAA574.

To register for this course, click here



STAA575: Applied Bayesian Statistics 02 Credits.

Prerequisite: STAA552, STAA562 and STAA567. (A background including CSSA and MSSA is assumed.)

Bayesian analysis of statistical models, prior and posterior distributions, computing methods, interpretation.

Textbook: Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians, by Christensen, Johnson, Branscum & Hanson.

Computer Software: R and either openBUGS (available at http://www.openbugs.net/w/FrontPage) or JAGS (Mac users will prefer JAGS, http://mcmc-jags.sourceforge.net/)

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on homework sets and a proctored cumulative final exam.

Credit allowed for only one course: STAT675K, STAA575.

To register for this course, click here



STAA576: Methods in Environmental Statistics 02 Credits.

Prerequisites: STAA552 and STAA561. Note: R programming skills (CSSA) are expected.

This course will introduce a number of statistical methodologies that are used in environmental and ecological studies. Students are introduced to topics in spatial statistics, and abundance estimation for biological populations.

Textbook: Course notes will be provided.

Computer Software: R

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on homework sets, a proctored mid-term exam, and a proctored cumulative final exam.

Credit allowed for only one course: STAT523, STAA576.

To register for this course, click here



STAA577: Statistical Learning and Data Mining 02 Credits.

Prerequisites: STAA551 and STAA561. Note: R programming skills (CSSA) are expected.

Regularization, prediction, regression, classification and clustering. Students will learn to implement modern statistical techniques for analyzing the types of data that would be encountered by statisticians working in business, medicine, science and government.

Textbook: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics), by James, Witten, Hastie & Tibshirani. ISBN-13: 978-1461471370, ISBN-10: 1461471370.

Computer Software: R

Proctor: This course requires an approved proctor for exams.

Grading: Course grade is based on weekly homework sets, three proctored (bi-weekly) quizzes, and a proctored cumulative final exam.

Note: This course is allowed in place of STAA576 for the MAS degree.

To register for this course, click here



Back to Distance MAS

 

 

Contact the Statistics Department Online Learning Program Office:

Phone: (970) 491-5268
Fax: (970) 491-1084
Email: stats_ddp@mail.colostate.edu
Follow us on Twitter: @CSUSTATDistance

Mailing Address:
Online Learning Office
Statistics Department
Colorado State University
1877 Campus Delivery
Fort Collins, CO 80523-1877

Jana Anderson, Associate Professor
Advisor for MAS and Certificate Programs
Department
of Statistics