Biology

BIO 4425 Biometry

Basic principles of statistical methods as applied to biological problems such as sampling techniques, analysis of data, experimental design and population dynamics. Emphasis will be on practical application. Prerequisites: BIO 2450 with a grade of "C" or higher, MATH 1315.

BIO 7368 Mathematical Modeling of Aquatic Resources and Ecosystems

Application of mathematical modeling, including regression and correlation analysis and systems modeling of natural processes, to sustainable aquatic resource and ecosystem issues. Computer applications emphasized. Prerequisite: MATH 2471/2472, or equivalent.

BIO 7405 Statistics and Experimental Design I

Introduction to inferential statistics, including exploratory and confirmatory data analysis, estimation and hypothesis testing, analysis of variance and regression, and non=parametric techniques, as applied to aquatic resource issues. Computer applications emphasized.

BIO 7406 Statistics and Experimental Design II

Introduction to the principles of experimental design, including randomization, replication, sample-size determination, completely randomized and randomized block design, factorial design, repeated measure design, and analysis of variance and covariance, as applied to aquatic resource issues. Computer applications emphasized. Prerequisite: BIO 7305 or consent of instructor.

Communication Studies

COMM 3301 Undergraduate Research Methods

An introductory research methods course for undergraduate students in communication studies. The goal is to help students become consumers of research.

COMM 5301 Graduate Research Methods

A required graduate course in Communication Studies geared at introducing students to basic methods in both qualitative and quantitative research.

COMM 5315 Research Practicum

A graduate course created to allow graduate students an opportunity to carry out a research study to produce both a convention and publication-worthy product.

Counseling, Leadership, Adult Education and School Psychology

ED 7351 Beginning Quantitative Research Design & Statistical Analysis

An introductory quantitative research course for Ph.D. students in Education. Primarily focused on design of high quality research, it also introduces the students to descriptive statistics and some basic statistical testing.

Criminal Justice

CJ 3347 Statistics for Criminal Justice

Introduction to social statistics to include univariate, bivariate, and multivariate descriptive and inferential statistics.

CJ 5325 Statistics for Criminal Justice

Introduction to social statistics to include univariate, bivariate, and multivariate descriptive and inferential statistics, with an emphasis on multivariate linear regression.

CJ 7321 Linear Regression

Multivariate linear regression, its considerations, assumptions, and diagnostics. Logistic regression is also introduced. A doctoral-level course for students who actually intend to engage in quantitative research.

CJ 7350A Forecasting, Trend Analysis, and Data Interpretation

The primary focus will be on Autoregressive Integrated Moving Average (ARIMA) modeling techniques. Specifically, we use ARIMA procedures and the RATS statistical package to: (1) build univariate ARIMA models to forecast time series data, (2) build univariate and bivariate ARIMA models to perform causal analyses between two or more time series, and (3) build univariate and intervention ARIMA models to assess the impact of ongoing natural experiments (interrupted time series analysis) on outcome series. Prerequisites: CJ 7321 or its equivalent or approval/permission of both the Instructor and the Doctoral Coordinator.

CJ 7350E Discrete Multivariate Models

This course focuses on regression models for discrete outcome variables, sometimes called limited or categorical dependent variables. Topics include maximum likelihood estimation, binary and multinomial logistic models and negative binomial models. Prerequisites: CJ 7321 or its equivalent or approval/permission of both the Instructor and the Doctoral Coordinator.

CJ 7350I Introduction to Structural Equation Modeling

The course provides an introduction to structural equation modeling, which is sometimes called mean and covariance structure analysis or latent variable analysis. The general topics include recursive and nonrecursive models, path analysis, measurement models, and factor analysis. Prerequisite: CJ 7321 or its equivalent or approval/permission of both the Instructor and the Doctoral Coordinator.

Curriculum and Instruction

CI 5390 Research Seminar in Education

Broad overview and application of quantitative and qualitative research designs. In this course, you will learn procedures for studying and improving education through critical reviews, analyses, and syntheses of scholarly research literature and other resources. Major topics covered in the course include the following: (a) quantitative, qualitative, and applied research techniques; (b) grant writing and reviewing; and (c) planning, writing, and presenting scholarly research.

Finance and Economics

ECO 4313 Econometrics

Introduction to econometric modeling, including regression, vector autoregressive and error correction models for time-series modeling and forecasting

Health Information Management

HIM 4330 Analysis and Interpretation of Healthcare Data

Collection, analysis, display interpretation and management of healthcare data. Definitions, sources, computations, reporting systems and methods of quality statistical process control will be explored as they relate to the management of health information. The use of data in research will also be explored.

Management

MGT 5390 Business Research Methods

This course focuses on data collection techniques (e.g. experiments, surveys, ethnographic research) and data analysis techniques (e.g. bivariate correlation, t-tests, one-way ANOVA, multiple regression with categorical and continuous predictors)

Mathematics

MATH 2328 Elementary Statistics

An algebra-based introductory probability and statistics course with topics chosen from descriptive statistics, sampling methods, probability, discrete and continuous distributions, confidence intervals and tests of hypothesis for population mean(s) and proportion(s), and use of TI-83/84 calculator for finding probabilities and make inferences.

MATH 3305 Intro to Probability and Statistics

A calculus-based probability and statistics course with topics chosen from sample space, events, probability, conditional probability, discrete and continuous random variables, univariate and bivariate distributions of random variables, mathematical expectations, moment-generating functions, distributions of functions of random variables, and use of TI-83/84 calculator for various distributions.

MATH 4305 Probability and Statistics

A calculus-based course with topics including central limit theorem, types and assessments of point estimators, confidence intervals and tests of hypothesis for population mean(s), proportion(s) , and variance(s), goodness-of-fit tests, and statistical inferences in simple linear regression.

MATH 5345 Regression Analysis

Topics include simple and multiple linear regression models, least squares estimation, residual analysis, multicollinearity, transformations, regression approach for analyzing data from designed experiments and criteria for model selections. Both the development of inferential procedures and their applications to real-world data are fully discussed. Students will also gain experiences in the use of statistical packages R and the writing of reports analyzing data.