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

Curriculum

The master's program offers the option of taking online statistics courses part time or full time. Full-time students can complete the program in less than a year. Part-time students can complete the program in 2, 3, or 4 years, depending on the preferred Plan of Study. Each semester is split into two 8-week sessions called subterms.

  • Subterm 1 – summer
  • Subterm 2 – 1st half of fall
  • Subterm 3 – 2nd half of fall
  • Subterm 4 – 1st half of spring
  • Subterm 5 – 2nd half of spring
  • Subterm 6 – 1st half of summer

Take online statistics courses in a customized plan of study

Choose a plan of study that works best with your schedule and goals. Each credit of coursework will require 6 hours per week of a student’s time, on average. Use this to gauge which option is right for you. If you choose to alter your personal plan of study, please be sure to confirm that courses will be offered and your prerequisites will be completed.

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.

The following are recommended plans and schedules for your applied statistics online courses to help guide the completion of your master's degree.

One-Year Plan of Study

First Academic Year

Subterm 1
  • (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. STAT 500 may be taken in place of GSLL 3096.

  • (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. GSLL 3096 may be taken in place of STAT 500.

Subterm 2
  • (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.

  • (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
  • (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
  • (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
  • (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. STAA 577 may be taken in place of STAA 576.

  • (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. STAA 576 may be taken in place of STAA 577.

Subterm 6
  • (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.

Two-Year Plan of Study

First Academic Year

Subterm 1
  • (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. STAT 500 may be taken in place of GSLL 3096.

  • (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. GSLL 3096 may be taken in place of STAT 500.

Subterm 2
  • (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.

Subterm 3
  • (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.

Subterm 4
  • (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.

Subterm 5
  • (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.)

    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.

Second Academic Year

Subterm 2
  • (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.

  • (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
  • (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.)

    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
  • (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.

  • (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
  • (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.

Subterm 6
  • (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.

Three-Year Plan of Study

First Academic Year

Subterm 1
  • (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. STAT 500 may be taken in place of GSLL 3096.

  • (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. GSLL 3096 may be taken in place of STAT 500.

Subterm 2
  • (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.

Subterm 3
  • (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.

Subterm 4
  • (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.

Subterm 5
  • (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.

Second Academic Year

Subterm 2
  • (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.

Subterm 3
  • (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.

  • (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.

Subterm 4
  • (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.)

    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
  • (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.

Third Academic Year

Subterm 2
  • (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
  • (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
  • (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.

Subterm 5
  • (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.

Subterm 6
  • (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.

Four-Year Plan of Study

First Academic Year

Subterm 1
  • (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. STAT 500 may be taken in place of GSLL 3096.

  • (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. GSLL 3096 may be taken in place of STAT 500.

Subterm 2
  • (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.

Subterm 3
  • (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.

Subterm 4
  • (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.

Subterm 5
  • (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.

Second Academic Year

Subterm 2
Subterm 3
  • (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.

Subterm 4
  • (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.

Subterm 5
  • (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.

Third Academic Year

Subterm 2
  • (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.

Subterm 3
  • (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.

Subterm 4
  • (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

Fourth Academic Year

Subterm 2
  • (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
  • (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
  • (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.

Subterm 5
  • (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
  • (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.

What Next?

  • Keep reading
    Next: Faculty »

    Get to know the respected, expert faculty behind the program's curriculum development, teaching, and research.

Loading...