Applied Statistics Master of Applied Statistics (M.A.S.)

Curriculum

The program offers you the option of taking classes part-time or full-time. It can be completed in less than a year for full-time students and less than two years for part-time students. All students start subterm 1 with two noncredit courses that are required as an intense review for coursework to follow. GSLL 3096 and STAT 500 (a credit version of GSLL 3096) are also offered in the spring.

Full-time students will take three 2-credit courses and one 1-credit course (7 credits) in subterms 2, 3, 4, and 5, totalling 28 credits. During subterm 6, students complete the 3-credit capstone course. Part-time students will take 3-4 credits per subterm, and will wait to take the final capstone course until all other coursework has been completed.

Subterm 1

Subterm 1 is ten weeks, beginning in mid-June. These noncredit courses are required of all students.

  • (0 cr.)

    Intensive review of mathematical methods that will be used in the program, including, but not limited to, differential and integral calculus, chain rule, L'Hôpital's rule, integration by parts, Taylor's theorem, multiple integrals, sequences and series, limits, linear algebra, and matrix theory.

  • (0 cr.)

    Software packages, graphics, and programming using R, SAS, other popular packages. This course is offered in both the Summer (Subterm 1) and Spring (Subterm 4).

Subterm 2

Subterm 2 is the first eight weeks of the Fall term beginning in August and ending in mid-October.

  • (2 cr.)

    Estimation and hypothesis testing methods in linear models, including t-tests, ANOVA, regression, and multiple regression. Residual analyses, transformations, goodness of fit, interaction and confounding. Implementation in SAS and R. Prerequisite: Admission to the M.A.S. program or written consent of instructor.

  • (2 cr.)

    Random variables, continuous and discrete distributions, expectations, joint and conditional distributions, and transformations. Applications to capture/recapture, financial and industrial models. Prerequisite: Admission to M.A.S. program or written consent of instructor.

  • (1 cr.)

    Exploratory data analysis using graphics, effective communication with graphs, and data reduction methods. Prerequisite: Admission to M.A.S. program or written consent of instructor.

  • STAA 571Survey Statistics (2 cr.)

    Survey design, simple random, stratified and cluster samples, and estimation and variance estimation. Prerequisite STAA 551 or STAT 540; STAA 562 or STAT 530 or written consent of instructor.

  • (2 cr.)

    Rank-based methods, nonparametric inferential techniques, scatter-plot smoothing, nonparametric function estimation, and environmental, bioscience applications. Prerequisite: (STAA 551 or concurrent registration or STAT 540; STAA 562 or concurrent registration or STAT 530) or written consent of instructor.

Subterm 3

Subterm 3 is the last eight weeks of Fall term, beginning in mid-October and ending in mid-December.

  • (2 cr.)

    Nonlinear regression, iteratively reweighted least squares, dose-response models, count data, multi-way tables, and survival analysis. Prerequisite: STAA 551 or concurrent registration or STAT 540 or written consent of instructor.

  • (2 cr.)

    Theory and applications of estimation, testing, confidence intervals, computer simulations, and sampling from the normal distribution. Prerequisite: STAA 561 or concurrent registration or STAT 520 or written consent of instructor.

  • (1 cr.)

    Sampling methods, simulating distributions of test statistics, and optimization. Prerequisite: (STAA 551 or concurrent registration or STAT 540; STAA 561 or concurrent registration or STAT 520) or written consent of instructor.

  • (2 cr.)

    Moving average and auto-regressive correlation structures, estimation and forecasting, modeling seasonality, and financial and environmental applications. Prerequisite: (STAA 551 or concurrent registration or STAT 540; STAA 561 or concurrent registration or STAT 520) or written consent of instructor.

Subterm 4

Subterm 4 is the first eight weeks of Spring term, beginning in mid-January and ending in mid-March.

  • (2 cr.)

    Estimation and hypothesis testing methods in linear models, including t-tests, ANOVA, regression, and multiple regression. Residual analyses, transformations, goodness of fit, interaction and confounding. Implementation in SAS and R. Prerequisite: Admission to the M.A.S. program or written consent of instructor.

  • (2 cr.)

    Analysis of variance, covariance, randomized block, latin square, split-plot, factorial, balanced, and unbalanced designs; applications to agriculture and biosciences; and implementation in SAS and R. Prerequisite: (STAA 551 or STAT 540; STAA 562 or STAT 530) or written consent of instructor.

  • (1 cr.)

    Confounding types of bias such as selection bias and regression effect bias, Simpson's paradox, experiments versus observational studies, etc. Prerequisite: Concurrent registration in STAA 551 or written consent of instructor.

  • (2 cr.)

    This course covers survey design, simple random, stratified and cluster samples, and estimation and variance estimation. Prerequisite: (STAA 551 or STAT 540; STAA 562 or STAT 530) or written consent of instructor.

  • (2 cr.)

    Bayesian analysis of statistical models, prior and posterior distributions, computing methods, and interpretation. Prerequisite: (STAA 552; STAA 562 or STAT 530; STAA 567) or written consent of instructor.

Subterm 5

Subterm 5 is the last eight weeks of Spring term, beginning in mid-March and ending in early May.

  • (0 cr.)

    Software packages, graphics, and programming using R, SAS, other popular packages. This course is offered in both the Summer (Subterm 1) and Spring (Subterm 4).

  • (1 cr.)

    Comparison, evaluation, and use of computer packages for univariate and multivariate statistical analyses. This course covers similar content to GSLL 3096, but is offered for 1 credit for students who must enroll in a credit-based course.

  • (2 cr.)

    Topics in linear, generalized linear, and nonlinear models with fixed and random predictors, and balanced and unbalanced cases. Statistical topics integrated with the use of the computer packages SAS and R. Prerequisite: STAA 553 or concurrent registration or written consent of instructor.

  • (1 cr.)

    Quality management, process control, reliability, and decision making. Prerequisite: (STAA 553 or concurrent registration; STAA 561 or STAT 520) or written consent of instructor.

  • (2 cr.)

    Multivariate ANOVA, principal components, factor analysis, cluster analysis, and discrimination analysis. Prerequisite: (STAA 551 or STAT 540; STAA 561 or STAT 520) or written consent of instructor.

  • (2 cr.)

    This course introduces a number of statistical methodologies that are used in environmental and ecological studies. You are introduced to topics in spatial statistics and abundance estimation for biological populations. Prerequisite: (STAA 552; STAA 561 or STAT 520) or written consent of instructor.

  • (2 cr.)

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

Subterm 6

Subterm 6 is the first six weeks of Summer term, beginning in mid-May and ending in late June.

  • (3 cr.)

    Consultant-client interactions, communications, and ethical practices. Complete a consulting project and provide a report. Prerequisite: 28 credits of STAA coursework or written consent of instructor.

Transfer credit

If you have taken prior master's level coursework in statistics, those credits may be considered for transfer into the M.A.S. program, if:

  • the grade earned was at least a B
  • the course was of the appropriate type and level for the M.A.S. program
  • courses requested for transfer were completed within ten years of the completion of your M.A.S.
  • those courses were not used to fulfill requirements for a previously earned degree (even as an elective)

A petition for transfer credit should be submitted along with the application materials indicated above. Note that a minimum of 24 credits must be earned through CSU, 21 of which must be earned after admission to Graduate School. See Transfer Credit Requirements – Graduate School.

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