Statistics (STAT)

STAT 8005  STATISTICAL METHODS I (3 credits)

Distributions; introduction to measures of central value and dispersion; population and sample; the normal distribution; inference: single population, inference: two populations; introduction to analysis of variance. Statistical packages on the computer will also be utilized in the course. Cross-listed with STAT 3000)

Prerequisite(s)/Corequisite(s): MATH 1310 or equivalent.

STAT 8015  STATISTICAL METHODS II (3 credits)

Regression and correlation, analysis of covariance, chi-square type statistics, more analysis of variance, questions of normality, introduction to non-parametric statistics. Statistical packages are used when appropriate. (Cross-listed with STAT 3010)

Prerequisite(s)/Corequisite(s): STAT 3000 or STAT 8005 or equivalent.

STAT 8416  INTRODUCTION TO DATA SCIENCE (3 credits)

Topics covered in this course include Data Technology, Methods of gathering and cleaning structured or unstructured data, Exploratory data analysis & Dynamic and interactive data visualization, Modeling data for prediction, forecasting or classification. (Cross-listed with STAT 4410)

Prerequisite(s)/Corequisite(s): MATH 4750 with a C- or better or STAT 3800 with a C- or better or permission of instructor. Students planning to enroll in this course should be comfortable with computer programming & have knowledge of data structures & preliminary statistical methods.

STAT 8426  EXPLORATORY DATA VIZUALIZATION AND QUANTIFICATION (3 credits)

Topics covered in this course include Exploratory Data Visualization for categorical/qualitative single/multivariate data, Grammar of Graphics, Organizing Data for Visualization, Methods of Displaying Data that include dynamic and interactive visualization, Visual Diagnostics of Statistical Models and Visual Statistical Inference. Students planning to enroll in this course should be comfortable with computer programming and have knowledge of data structures and preliminary statistical methods. (Cross-listed with STAT 4420)

Prerequisite(s)/Corequisite(s): MATH 4750 or MATH 8756 w/ a grade of C- or better or STAT 3800 or STAT 8005 w/ a C- or better or another introductory probability/statistics course w/ a C- or better, & CSCI 1620 or equivalent with a grade of C- or better, or permission of instructor.

STAT 8436  LINEAR MODELS (3 credits)

This is an introduction to linear statistical models which will include: simple linear regression models, multiple linear regression models, ANOVA models including one way ANOVA, randomized block design,and other designs. Also, logistic regression models, Poisson regression models, bootstrapping/resampling models, survival analysis. Some necessary linear algebra and mathematical statistics ideas will be covered in the course also. If time allows, some mixed models and/or survival models. Much use of computer software will be made. (Cross-listed with STAT 4430)

Prerequisite(s)/Corequisite(s): MATH 4750 or MATH 8756 w/ a grade of C- or better or STAT 3800 or STAT 8805 w/ a C- or better or instructor permission based on students' having taken a basic statistics course w/ a grade of C- or better & having at least a basic knowledge of calculus.

STAT 8446  TIME SERIES ANALYSIS (3 credits)

The objective of this course is to learn and apply statistical methods for the analysis of data that have been observed over time. Topics covered include: Models for Stationary and Non-Stationary Time Series, Model Specification, Parameter Estimation, Model Diagnostics, Forecasting, Seasonal Models, Time Series Regression, and Spectral Analysis. Statistical software will be used. (Cross-listed with STAT 4440)

Prerequisite(s)/Corequisite(s): MATH 4750 or MATH 8756 w/ a grade of C- or better or STAT 3800 or STAT 8005 w/ a C- or better or another introductory probability/statistics course w/ a C- or better, & CSCI 1620 or equivalent with a grade of C- or better, or permission of instructor.

STAT 8700  BAYESIAN STATISTICS (3 credits)

The objective of this course is to introduce the Bayesian approach to statistical inference. Topics covered include: Review of probability, Bayes theorem, and Likelihood; The Bayesian methodology, prior and posterior distributions; Choices of prior distribution, conjugate and Jeffreys priors; Credible intervals and inference; Bayesian computation - Markov Chain Monte Carlo and the Gibbs Sampler; Hierarchical models; Regression models.

Prerequisite(s)/Corequisite(s): MATH 8756 or equivalent or permission of instructor.

STAT 8710  DESIGN AND ANALYSIS OF EXPERIMENTS (3 credits)

Introduction to design and analysis of controlled experiments. The goal of experimental design is to be able to construct an experiment to identify which factors most impact the response and do so in an efficient manner. Statistical software will be used. Types of designs studied include: Randomized Block Designs, Latin Square Designs, Incomplete Block Designs, Factorial Designs, and Nested Designs.

Prerequisite(s)/Corequisite(s): MATH 4750/8756 or permission of instructor.

STAT 8805  APPLIED ENGINEERING PROBABILITY AND STATISTICS (3 credits)

An introduction to the application of probability and statistics to engineering problems. Topics include: probability and probability distributions, mathematical expectation, distribution of random variables, binomial, Poisson, hypergeometric, gamma, normal, and t-distributions, Central Limit Theorem, confidence intervals, hypothesis testing, linear regression, contingency tables. Credit for both MATH 4740 and STAT 3800 will not be given. (Cross-listed with STAT 3800)

Prerequisite(s)/Corequisite(s): MATH 1970

STAT 8960  MASTER'S PROJECT (1-6 credits)

An applied project, designed and executed under the supervision of both a faculty and industry advisor. In the project the student will apply their mathematical and/or statistical skills to an applied problem. The student will present their results via a written report and oral presentation. (Cross-listed with MATH 8960).

Prerequisite(s)/Corequisite(s): Permission of faculty advisor and graduate program chair. Not open to non-degree graduate students.