Joshua Habiger

An introductory course in the theory and methods of statistics. Descriptive measures, elementary probability, sampling, estimation, hypothesis testing, correlation and regression. Same course as STAT 2023 or STAT 2053.Prerequisite(s): MATH 1483 or higher, except MATH 1493, with a grade of 'C' or better; or an acceptable placement score (see mathplacement.okstate.edu).

An introductory course in the theory and methods of statistics. Descriptive measures, elementary probability, sampling, estimation, hypothesis testing, correlation and regression. Same course as STAT 2023 or STAT 2053.Prerequisite(s): MATH 1483 or higher, except MATH 1493, with a grade of 'C' or better; or an acceptable placement score (see mathplacement.okstate.edu).

R dataset construction, elementary statistical analysis, and use of statistics and graphics with R. May not be used for degree credit with STAT 4193, STAT 5191, STAT 5193.Prerequisite(s): STAT 4013 or equivalent.

R dataset construction, elementary statistical analysis, and use of statistics and graphics with R. May not be used for degree credit with STAT 4191, STAT 4193, STAT 5193.Prerequisite(s): STAT 4013 or STAT 5013.

Directed research culminating in the PhD thesis.Offered for variable credit, 1-10 credit hours, maximum of 30 credit hours.Prerequisite(s): Consent of advisory committee.

Methods of research and supervised thesis or report.Offered for variable credit, 1-6 credit hours, maximum of 6 credit hours.Prerequisite(s): Consent of advisory committee.

SAS and R dataset construction, elementary statistical analysis, and use of statistics and graphics with SAS and R. May not be used for degree credit with STAT 4091, STAT 4191, STAT 5091, STAT 5191, STAT 5193.Prerequisite(s): STAT 4013 or equivalent.

SAS and R dataset construction, elementary statistical analysis, and use of statistics and graphics with SAS and R. May not be used for degree credit with STAT 4091, STAT 4191, STAT 4193, STAT 5191, STAT 5091.Prerequisite(s): STAT 5013 or equivalent.

Computationally intense statistical methods for prediction and classification with R. Topics are bias-variance tradeoff; prediction and classification error; cross validation; bootstrapping; linear and logistic regression; discriminant functions; k-nearest neighbors; local and spline-based regression; generalized additive models; model selection and regularization; support vector machines; decision trees; principle component analysis; cluster analysis. May not be used for degree credit with STAT 4463.Prerequisite(s): STAT 5543.

Computationally intense statistical methods for prediction and classification with R. Topics are bias-variance tradeoff; prediction and classification error; cross validation; bootstrapping; linear and logistic regression; discriminant functions; k-nearest neighbors; local and spline-based regression; generalized additive models; model selection and regularization; support vector machines; decision trees; principle component analysis; cluster analysis. May not be used for degree credit with STAT 4463.Prerequisite(s): STAT 5543.

Computationally intense statistical methods for prediction and classification with R. Topics are bias-variance tradeoff; prediction and classification error; cross validation; bootstrapping; linear and logistic regression; discriminant functions; k-nearest neighbors; local and spline-based regression; generalized additive models; model selection and regularization; support vector machines; decision trees; principle component analysis; cluster analysis. May not be used for degree credit with STAT 5063.Prerequisite(s): STAT 4043.

An introductory course in the theory and methods of statistics. Descriptive measures, elementary probability, sampling, estimation, hypothesis testing, correlation and regression. Same course as STAT 2023 or STAT 2053.Prerequisite(s): MATH 1483 or higher, except MATH 1493, with a grade of 'C' or better; or an acceptable placement score (see mathplacement.okstate.edu).

R dataset construction, elementary statistical analysis, and use of statistics and graphics with R. May not be used for degree credit with STAT 4191, STAT 4193, STAT 5193.Prerequisite(s): STAT 4013 or STAT 5013.

Directed research culminating in the PhD thesis.Offered for variable credit, 1-10 credit hours, maximum of 30 credit hours.Prerequisite(s): Consent of advisory committee.

SAS and R dataset construction, elementary statistical analysis, and use of statistics and graphics with SAS and R. May not be used for degree credit with STAT 4091, STAT 4191, STAT 5091, STAT 5191, STAT 5193.Prerequisite(s): STAT 4013 or equivalent.

SAS and R dataset construction, elementary statistical analysis, and use of statistics and graphics with SAS and R. May not be used for degree credit with STAT 4091, STAT 4191, STAT 4193, STAT 5191, STAT 5091.Prerequisite(s): STAT 5013 or equivalent.

Random variable generation; numerical calculations of maximum likelihood estimators, computer intensive exact tests; randomized tests; bootstrap and cross validation methods, Monte Carlo integration and simulation; Markov Chain Monte Carlo methods for Bayesian estimation.Prerequisite(s): STAT 5223.

An introductory course in the theory and methods of statistics. Descriptive measures, elementary probability, sampling, estimation, hypothesis testing, correlation and regression. Same course as STAT 2023 or STAT 2053.Prerequisite(s): MATH 1483 or higher, except MATH 1493, with a grade of 'C' or better; or an acceptable placement score (see mathplacement.okstate.edu).

Directed research culminating in the PhD thesis.Offered for variable credit, 1-10 credit hours, maximum of 30 credit hours.Prerequisite(s): Consent of advisory committee.

Directed practicum or internship experience in a Statistics-related professional work setting. Students must have an approved internship that will provide statistical experience beyond that available in the classroom. Students produce written analyses of their work and learning under the guidance of the instructor and internship site supervisor.Offered for variable credit, 1-9 credit hours, maximum of 9 credit hours.Prerequisite(s): Consent of instructor.

An introductory course in the theory and methods of statistics. Descriptive measures, elementary probability, sampling, estimation, hypothesis testing, correlation and regression. Same course as STAT 2023 or STAT 2053.Prerequisite(s): MATH 1483 or higher, except MATH 1493, with a grade of 'C' or better; or an acceptable placement score (see mathplacement.okstate.edu).

R dataset construction, elementary statistical analysis, and use of statistics and graphics with R. May not be used for degree credit with STAT 4191, STAT 4193, STAT 5193.Prerequisite(s): STAT 4013 or STAT 5013.

Directed research culminating in the PhD thesis.Offered for variable credit, 1-10 credit hours, maximum of 30 credit hours.Prerequisite(s): Consent of advisory committee.