# Statistics

## STAT 2163: Introduction to Statistical Methods

ACTS Common Course - MATH 2103

Prerequisites: MATH 1003, MATH 1113, or consent of the instructor.

Descriptive statistics, random variables, probability and sampling distributions,
estimation, hypothesis testing, regression, analysis of variance, non-parametric techniques.

Note: May not be taken for credit after completion of STAT 3153.

Note: A grade of C of better must be earned in this course if being used to satisfy
the general education mathematics requirement.

## STAT 2304: Programming Languages for Data Science

Prerequisite: Any introductory statistics course.

The goal of this course is to introduce the basic computer programming using Python
and R to the undergraduate students who are interested in working in the rapidly growing
fields of data science and data analytics. Python and R are two open-source programming
languages with a large data scientist community. This course will introduce the basic
programming skills and tools necessary to efficiently collect, process, visualize,
and analyze the datasets. Hands-on projects will be given to help students gain experience
with software packages.

## STAT 3113: Regression Analysis

Prerequisite: Any introductory statistics course or permission of instructor.

This course introduces the methods for fitting and interpreting regression models.
Topics include simple linear regression (SLR), multiple linear regression (MLR), model
checking, variable selection methods, dummy variables, diagnostic measures, logistic
regression, and time series analysis. Instruction will include the use of statistical
programming language.

## STAT 3153: Applied Statistics

Prerequisite: MATH 2924

A balanced approach emphasizing both theory and applications will be taken. Topics
include descriptive statistics, exploratory data analysis, probability and probability
models, discrete and continuous random variables, confidence intervals, hypothesis
testing, and control charts. Students will be required to collect data, use a current
statistical software package to analyze the data, and make inferences based upon the
data analysis as part of an individual and/ or group project.

Note: A grade of C or better must be earned in the course used to satisfy the general
education mathematics requirement.

## STAT 3183: Statistical Process Control

Offered: Spring

Prerequisite: An introductory statistics course or permission of instructor.

Statistical process control is an important topic for anyone interested in applying
statistics in industry. This course focuses on theory and methods of quality monitoring
including process capability, control charts, acceptance sampling, quality engineering,
and quality design.

## STAT 3203: Actuarial Probability I

Offered: Fall

Prerequisite: MATH 2934

In this course we develop knowledge of the fundamental probability tools for quantitatively
assessing risk. The application of these tools to problems encountered in actuarial
science is emphasized. A thorough command of the supporting calculus is assumed. A
very basic knowledge of insurance and risk management is assumed.

## STAT 3213: Actuarial Probability II

Offered: Spring

Prerequisite: STAT 3203

This course is a continuation to STAT 3203. At the end of the course, a students is
prepared to take Exam P of the Society of Actuaries.

## STAT 4113: Categorical Data Analysis

Offered: Fall

Prerequisite: STAT 3113 or permission of instructor.

Statistical tools to analyze univariate and multivariate categorical responses. Emphasis
is given to Generalized Linear Models, including logistic regression and loglinear
models.

## STAT 4153: Experimental Design and Analysis

Prerequisite: Any introductory statistics course or permission of instructor, and
junior standing or above.

This course introduces students to both design and analysis of experiments as well
as statistical computing. Emphasis is given to develop an understanding of experimental
methods and major experimental designs. Students will be required to design and carry
out an experiment, use a current statistical software package to analyze the data,
and make inferences based upon the analysis.

## STAT 4163: Mathematical Statistics

Prerequisite: STAT 3153

This is an introductory course in mathematical statistics. Topics include distribution
functions (both discrete and continuous), multivariate distributions, distributions
of functions of random variables, and statistical inference.

Note: A grade of C or better must be earned in the course used to satisfy the general
education mathematics requirement.

## STAT 4173: Advanced Biostatistics

Prerequisites: An introductory statistics course or permission of instructor.

This course will include analysis of variance, one factor experiments, experimental
design with two or more factors, linear and multiple regression analysis, and categorical
data analysis.

Note: A grade of C or better must be earned in the course used to satisfy the general
education mathematics requirement.

## STAT 4283: Financial Mathematics I

Offered: Fall

Prerequisite: MATH 2914

This is an introductory course in Financial Mathematics. The student will learn about
the different types of interest (simple interest, discount interest, compound interest),
annuities, debt retirement methods, and investing in stocks and bonds.

## STAT 4293: Financial Mathematics II

Offered: Spring

Prerequisite: MATH 4283

This is a continuation of STAT 4283. Topics include loans, bonds, cash flow and portfolios,
immunization, derivatives and options. At the end of this course, a student is prepared
to take Exam FM of the Society of Actuaries.

## STAT 4383: Machine Learning

Offered: Fall

Prerequisites: MATH 2914 and 4003

This course is directed towards advanced undergraduates in statistics, mathematics,
or related quantitative fields. The focus of the course is an accessible overview
of the field of machine learning and provide the students with valuable hands-on experience
by illustrating how to implement each of the machine learning methods using Python.
Topics covered include Decision Tree, Support Vector Machines, and the kernel methods,
AdaBoost and GBDT method, Logistic regression, and neural network, and more.

## STAT 4393: Statistical Learning

Offered: Spring

Prerequisite: STAT 3113 or permission of instructor.

This course is directed towards advanced undergraduates or master's students in statistical
or related quantitative fields. The focus of the course is an accessible overview
of the field of statistical learning and provide the students with valuable hands-on
experience by illustrating how to implement each of the statistical learning methods
using R or other statistical programming language. Topics covered include: regression
techniques, classification methods, linear model selection and regularization, unsupervised
learning, and more.