2021-2022 Course List

Curricular Practical Training: Co-Operative Experience is a zero-credit full-time practical training experience for one semester and an adjacent fall or spring term. Special rules apply to preserve full-time student status. Please contact an advisor in your program for complete information.

Prerequisites:

At least 60 credits earned; in good standing; instructor permission; co-op contract; other prerequisites may also apply.

Simple and multiple linear regression, model adequacy checking and validation, identification of outliers, leverage and influence, polynomial regression, variable selection and model building strategies, nonlinear regression, and generalized linear regression.

Prerequisites:

MATH 354 / STAT 354 or STAT 455 with “C” (2.0) or better or consent 

Randomized complete block design, Latin squares design, Graco- Latin squares design, balanced incomplete block design, factorial design, fractional factorial design, response surface method, fixed effects and random effects models, nested and split plot design.

Prerequisites:

MATH 354 / STAT 354 or STAT 455 with “C” (2.0) or better or consent 

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications. Includes probability, continuous probability distributions, multivariate distributions, functions of random variables, central limit theorem and statistical inference. Same as MATH 455. Prereq: MATH 223 with C or better or consent

Prerequisites:

MATH 223 with “C” (2.0) or better or consent

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications, including sufficient statistics, additional statistical inference, theory of statistical tests, inferences about normal models and nonparametric methods. Same as MATH 456. Prereq: MATH/STAT 455 with C or better or consent

Prerequisites:

MATH 455, STAT 455 with “C” (2.0) or better or consent

Sampling distributions: means and variances. Bias, robustness and efficiency. Random sampling, systematic sampling methods including stratified random sampling, cluster sampling and two-stage sampling, ratio, regression, and population size estimation. Suitable statistical software is introduced, for example, MATLAB, R, SAS, etc.

Prerequisites:

Either MATH/STAT 354 or both MATH 121 adn STAT 154 with "C" (2.0) or better, or consent.

Forms of multivariate analysis for discrete data, two dimensional tables, models of independence, log linear models, estimation of expected values, model selection, higher dimensional tables, logistic models and incompleteness. Logistic regression. Suitable statistical software is introduced, for example, MATLAB, R, SAS etc.

Prerequisites:

Either MATH/STAT 354 or both MATH 121 and STAT 154 with “C” (2.0) or better, or consent.

Derivation and usage of nonparametric statistical methods in univariate, bivariate, and multivariate data. Applications in count, score, and rank data, analysis of variance for ranked data. Nonparametric regression estimation. Suitable statistical software is introduced, for example, MATLAB, R, SAS, etc.

Prerequisites:

Either MATH/STAT 354 or both STAT 154 and MATH 121 with “C” (2.0) or better, or consent.

The study of a particular topic primarily based upon recent literature. May be repeated for credit on each new topic.

A course designed to upgrade the qualifications of persons on-the-job. May be repeated for credit on each new topic.

This course is designed to allow undergraduate students an opportunity to integrate their statistics experiences by engaging each student in working on problems in applied or theoretical statistics. Spring

Prerequisites:

STAT 457, STAT 458, STAT 459, STAT 450 (at least two of these)

A course in an area of statistics not regularly offered. May be repeated for credit on each new topic.

Provides a student the opportunity to gain expertise and experience in a special field under the supervision of a qualified person.

Independent individual study under the guidance and direction of a faculty member. Special arrangements must be made with an appropriate faculty member. May be repeated for credit of each new topic.

Simple and multiple regression, correlation, analysis of variance and covariance.

Prerequisites:

MATH 354 or STAT 354 or (MATH 455 or MATH 555) or (STAT 455 or STAT 555) with “C” (2.0) or better or consent.

Randomized complete block design, Latin squares design, Graco- Latin squares design, balanced incomplete block design, factorial design, fractional factorial design, response surface method, fixed effects and random effects models, nested and split plot design.

Prerequisites:

MATH 354 or STAT 354 or (MATH 455 or MATH 555) or (STAT 455 or STAT 555) with “C” (2.0) or better or consent.

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications. Includes probability, continuous probability distributions, multivariate distributions, functions of random variables, central limit theorem, and statistical inference. Same as MATH 555.

Prerequisites:

MATH 223 with “C” (2.0) or better or consent.

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications, including sufficient statistics, additional statistical inference, theory of statistical tests, inferences about normal models, and nonparametric methods. Same as MATH 556.

Prerequisites:

(MATH 455 or MATH 555) or (STAT 455 or STAT 555) with “C” (2.0) or better or consent.

Topics include: sampling distributions, means and variances; bias, robustness and efficiency; random sampling; systematic sampling methods including stratified random, cluster and two-state sampling; and ratio, regression, and population size estimation. Suitable software, such as MATLAB, R, SAS, etc., is introduced.

Prerequisites:

Either MATH/STAT 354 or both STAT 154 and MATH 121 with “C” (2.0) or better, or consent.

Topics on multivariate analysis for discrete data, including two/higher dimensional tables; models of independence; log linear models; estimation of expected values; model selection; and logistic models, incompleteness and regression. Suitable statistical software, such as MATLAB, R, SAS, etc., is introduced.

Prerequisites:

Either MATH/STAT 354 or both STAT 154 and MATH 121 with “C” (2.0) or better, or consent.

Topics include derivation and usage of nonparametric methods in univariate, bivariate, and multivariate data; applications in count, score, and rank data; analysis of variance for ranked data; and regression estimation. Suitable software, such as MATLAB, R, SAS, etc., is introduced.

Prerequisites:

Either MATH/STAT 354 or both STAT 154 and MATH 121 with “C” (2.0) or better, or consent.

The study of a particular topic primarily based upon recent literature. May be repeated for credit on each new topic.

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Provides a student the opportunity to gain expertise and experience in a special field under the supervision of a qualified person.

Bayesian Statistics is an alternative to Frequentist statistics. Bayesian inference uses probability for both hypotheses and data. In Bayesian statistics, population parameters are considered random variables having probability distributions. The probabilities measure a degree of belief in the parameters. Bayes¿ theorem is used to reformulate the beliefs using observed data. This course introduces the Bayesian approach to statistical inference and describes effective approaches to Bayesian modeling and computation.

Prerequisites:

MATH/STAT 455/555 and STAT 450/550, or consent