Department of Statistics

The science of statistics deals with the collection and summarization of data, design of experiments and surveys, measurement of the magnitude of variation in both experimental and survey data, estimation of population parameters with measures of their accuracy and precision, tests of hypotheses about populations and studies of the relationships between two or more variables. While the ideal of science is to achieve a systematic interrelationship of facts, scientific methods must be a pursuit of this ideal by experimentation, observation and logical arguments from various accepted postulates. Thus, the science of statistics is a set of scientific principles and methodologies that are useful in reaching conclusions about populations and processes when the available information is both limited and variable. Hence, statistical principles are useful in all the sciences, both physical and social. Many practical applications of statistics are found in a wide variety of fields, including biology, education, social sciences, engineering, business, government and agriculture.

The Department of Statistics offers training in statistics leading to degrees of Bachelor of Science, Master of Science and Doctor of Philosophy.

STAT 182 Foundations of Statistics

Credit 1. 1 Lecture Hour.

Elementary topics in statistics; data collection; design of experiments; confidence intervals, hypothesis testing; ethics in statistics; the role of statistics in industry, the health profession and the sciences.
Prerequisite: Statistics majors only.

STAT 201 Elementary Statistical Inference

Credits 3. 3 Lecture Hours.

(MATH 1342, 1442) Elementary Statistical Inference. Data collection, tabulation and presentation; elementary description of the tools of statistical inference; probability, sampling and hypothesis testing; applications of statistical techniques to practical problems; cannot be taken after or concurrently with any other course in statistics or SCMT 303. Only one of the following will satisfy the requirements for a degree: STAT 201, STAT 301, STAT 302 or STAT 303.

STAT 211 Principles of Statistics I

Credits 3. 3 Lecture Hours.

Introduction to probability and probability distributions; sampling and descriptive measures; inference and hypothesis testing; linear regression, analysis of variance.
Prerequisite: MATH 148, MATH 152, or MATH 172; also taught at Galveston campus.

STAT 212 Principles of Statistics II

Credits 3. 3 Lecture Hours.

Design of experiments, model building, multiple regression, nonparametric techniques and contingency tables.
Prerequisite: STAT 211.

STAT 301 Introduction to Biometry

Credits 3. 3 Lecture Hours.

Intended for students in animal sciences. Introduces fundamental concepts of biometry including measures of location and variation, probability, tests of significance, regression, correlation and analysis of variance which are used in advanced courses and are being widely applied to animal-oriented industry. Only one of the following will satisfy the requirements for a degree: STAT 201, STAT 301, STAT 302 or STAT 303.
Prerequisite: MATH 168 or MATH 166 or equivalent; junior or senior classification.

STAT 302 Statistical Methods

Credits 3. 3 Lecture Hours.

Intended for undergraduates in the biological sciences. Introduction to concepts of random sampling and statistical inference; estimation and testing hypotheses of means and variances; analysis of variance; regression analysis; chi-square tests. Only one of the following will satisfy the requirements for a degree: STAT 201, STAT 301, STAT 302 or STAT 303.
Prerequisite: MATH 168 or MATH 166 or equivalent; junior or senior classification.

STAT 303 Statistical Methods

Credits 3. 3 Lecture Hours.

Intended for undergraduates in the social sciences. Introduction to concepts of random sampling and statistical inference, estimation and testing hypotheses of means and variances, analysis of variance, regression analysis, chi-square tests. Only one of the following will satisfy the requirements for a degree: STAT 201, STAT 301, STAT 302 or STAT 303.
Prerequisite: MATH 168 or MATH 166 or equivalent; junior or senior classification; also taught at Galveston campus.

STAT 307 Sample Survey Techniques

Credits 3. 3 Lecture Hours.

Concepts of population and sample; the organization of a sample survey; questionnaire design. Basic survey designs and computation of estimates and variances.
Prerequisite: STAT 301 or STAT 302 or STAT 303 or SCMT 303.

STAT 312 Statistics for Biology

Credits 3. 3 Lecture Hours.

Statistical methods for biological applications including the topics multiple linear regression, experimental design, analysis of variance, categorical data analysis, nonparametric methods, general linear models, density estimation, nonlinear and logistic regression and inference for percentiles.
Prerequisites: MATH 147 or equivalent; STAT 201 or MATH 148, or equivalents.

STAT 335/CSCE 320 Principles of Data Science

Credits 3. 3 Lecture Hours.

Theoretical foundations, algorithms and methods of deriving valuable insights from data; includes foundations in managing and analyzing data at scale, e.g. big data; data mining techniques and algorithms; exploratory data analysis; statistical methods and models; data visualization.
Prerequisites: STAT 211 or ECEN 303; STAT 212 or CSCE 222/ECEN 222.
Cross Listing: CSCE 320/STAT 335.

STAT 404 Statistical Computing

Credits 3. 3 Lecture Hours.

Statistical programming in R and Python; random number generation; design of simulation studies; interactive and dynamic statistical graphics; parallel computing in statistics; statistical and machine learning algorithms.
Prerequisites: STAT 212; junior or senior classification or approval of instructor.

STAT 406 Design and Analysis of Experiments

Credits 3. 3 Lecture Hours.

Design fundamentals; completely randomized designs; blocking; factorial, nested, nested-factorial designs; incomplete designs; fractional factorial designs; confounding; general mixed factorials; split pilot; analysis of covariance; crossover designs; power analysis, sample size determination.
Prerequisite: STAT 212; STAT 408.

STAT 407 Principles of Sample Surveys

Credits 3. 3 Lecture Hours.

Principles of sample surveys and survey design; techniques for variance reduction; simple, stratified and multi-stage sampling; ratio and regression estimates; post-stratification; equal and unequal probability sampling.
Prerequisite: STAT 212.

STAT 408 Introduction to Linear Models

Credits 3. 3 Lecture Hours.

Introduction to the formulation of linear models and the estimation of the parameters of such models, with primary emphasis on least squares. Application to multiple regression and curve fitting.
Prerequisites: STAT 212; MATH 304 or MATH 323.

STAT 414 Mathematical Statistics I

Credits 3. 3 Lecture Hours.

Mathematical theory of statistics; probability, random variables and their distributions, transformations of random variables, expectations and variance, generating functions, sampling distributions and basic limit theorems.
Prerequisite: MATH 221, MATH 251 or MATH 253.

STAT 415 Mathematical Statistics II

Credits 3. 3 Lecture Hours.

Continuation of the mathematical theory of statistics, including principles for statistical inference, formulation of statistical models, reduction of data, point estimation, confidence intervals, hypothesis testing and Bayesian inference.
Prerequisite: STAT 414 or MATH 411.

STAT 421/CSCE 421 Machine Learning

Credits 3. 3 Lecture Hours.

Theoretical foundations of machine learning, pattern recognition and generating predictive models and classifiers from data; includes methods for supervised and unsupervised learning (decision trees, linear discriminants, neural networks, Gaussian models, non-parametric models, clustering, dimensionality reduction, deep learning), optimization procedures and statistical inference.
Prerequisites: Grade of C or better in MATH 304 and STAT 211; grade of C or better in CSCE 221 or STAT 404.
Cross Listing: CSCE 421/STAT 421.

STAT 426 Methods in Time Series Analysis

Credits 3. 3 Lecture Hours.

Autocorrelation and spectral characteristics of univariate, autoregressive and moving average models; identification, estimation and forecasting.
Prerequisites: STAT 408; STAT 414 or approval of instructor.

STAT 436 Multivariate Analysis and Statistical Learning

Credits 3. 3 Lecture Hours.

Matrix algebra; random vectors; multivariate distributions; copulas; multivariate generalizations of classical testing; principle component analysis; discriminant analysis; clustering; multidimensional scaling; factor analysis; canonical analysis.
Prerequisites: MATH 304 or MATH 323; STAT 212; STAT 415 or equivalent.

STAT 438 Bayesian Statistics

Credits 3. 3 Lecture Hours.

Analysis of scalar and vector-valued parameters; Bayesian linear models; Monte Carlo computational methods; prior elicitation; hypothesis testing and model selection; hierarchical models; selected advanced models; use of statistical packages such as WinBUGS, R or MATLAB.
Prerequisites: MATH 221; STAT 408 or equivalent.

STAT 445 Applied Biostatistics and Data Analysis

Credits 3. 3 Lecture Hours.

Applications of regression methods in biostatistics; correlated data analysis; survival analysis; missing data techniques; use of the R programming language.
Prerequisites: STAT 212; STAT 408 or approval of instructor.

STAT 446 Statistical Bioinformatics

Credits 3. 3 Lecture Hours.

Analysis of high-dimensional genomic and proteomic data using R; sequence analysis; genome-wide association studies; proteomics; array-based technologies; classification techniques.
Prerequisites: STAT 212; STAT 408 or approval of instructor.

STAT 459 Categorical Data Analysis

Credits 3. 3 Lecture Hours.

Techniques for the analysis of categorical data; contingency table analysis; logistic regression; Poisson regression; loglinear models; analysis of ordinal data; use of computer software such as SAS or R.
Prerequisite: STAT 212; STAT 408 or equivalent.

STAT 482 Statistics Capstone

Credits 3. 3 Lecture Hours.

Integration of statistical models, design, sampling, graphics and computing for the analysis of real problems; planning, drafting, revising and editing reports; ethics; principles of collaboration and communication.
Prerequisites: STAT 404; STAT 406; STAT 408 and senior classification.

STAT 484 Internship

Credits 0 to 3. 0 to 3 Other Hours.

Directed internship in an organization to provide on-the-job training and applied research experience with professionals in settings appropriate to statistics and student professional interest.
Prerequisites: Major in statistics; 12 completed hours of statistics; 2.5 cumulative GPA; 2.5 GPA in statistics courses; approval of statistics undergraduate advisor.

STAT 485 Directed Studies

Credits 1 to 6. 1 to 6 Other Hours.

Special problems in statistics not covered by another course in the curriculum. Work may be in either theory or methodology.
Prerequisite: Approval of instructor.

STAT 489 Special Topics in…

Credits 1 to 4. 1 to 4 Lecture Hours.

Selected topics in an identified area of statistics. May be repeated for credit.
Prerequisite: Junior or senior classification or approval of department head.

STAT 491 Research

Credits 1 to 4. 1 to 4 Lecture Hours. 0 to 4 Lab Hours.

Research conducted under the direction of faculty members in statistics. May be taken four times for credit. Registration in multiple sections of this course is possible within a given semester provided that the per semester credit hour limit is not exceeded.
Prerequisite: Junior or senior classification or approval of instructor.

Akleman, Derya G, Instructional Associate Professor
Statistics
PHD, Texas A&M University, 1996

Bhattacharya, Anirban, Associate Professor
Statistics
PHD, Duke University, 2012

Carroll, Raymond J, Distinguished Professor
Statistics
PHD, Purdue University, 1974

Chakrabortty, Abhishek, Assistant Professor
Statistics
PHD, Harvard Graduate School of Arts and Sciences, 2016

Chen, Willa W, Professor
Statistics
PHD, New York University, 2000

Cline, Daren B, Professor
Statistics
PHD, Colorado State University, 1983

Cummings, Shelby M, Lecturer
Statistics
MPH, Texas A&M University, 2017

Dabney, Alan R, Associate Professor
Statistics
PHD, University of Washington, 2006

Garcia, Tanya, Associate Professor
Statistics
PHD, Texas A&M University, 2011

Gaynanova, Irina, Assistant Professor
Statistics
PHD, Cornell University, 2015

Hart, Jeffrey D, Professor
Statistics
PHD, Southern Methodist University, 1981

Hatfield, Lloyd K, Senior Lecturer
Statistics
MBA, University of North Texas, 1980

Hernandez Magallanes, Irma Del Consue, Instructional Assistant Professor
Statistics
PHD, University California Berkley, 2010

Huang, Jianhua, Professor
Statistics
PHD, University of California, Berkeley, 1997

Johnson, Valen E, University Distinguished Professor
Statistics
PHD, University Of Chicago, 1989

Jones, David Edward, Assistant Professor
Statistics
PHD, Harvard, 2016

Jun, Mikyoung, Professor
Statistics
PHD, University of Chicago, 2005

Karmakar, Moumita, Instructional Assistant Professor
Statistics
PHD, University of Maryland at Baltimore, 2015

Katzfuss, Matthias S, Associate Professor
Statistics
PHD, The Ohio State University, 2011

Kincheloe, Faron, Visiting Professor
Statistics
MS, Baylor University, 2011

Kolodziej, Elizabeth Y, Instructional Assistant Professor
Statistics
PHD, Texas A&M University, 2010

Liang, Hwa Chi, Instructional Assistant Professor
Statistics
PHD, University of New Mexico, 2003

Liu, Shuling, Instructional Assistant Professor
Statistics
PHD, Emory University, 2015

Longnecker, Michael T, Professor
Statistics
PHD, Florida State University, 1976

Ma, Jing, Assistant Professor
Statistics
PHD, University of Michigan, 2015

Mallick, Bani K, University Distinguished Professor
Statistics
PHD, University of Connecticut, 1994

Newton, Howard J, Senior Professor
Statistics
PHD, SUNY Buffalo, 1975

Ni, Yang, Assistant Professor
Statistics
PHD, Rice University, 2015

Pati, Debdeep, Associate Professor
Statistics
PHD, Duke University, 2012

Pourahmadi, Mohsen, Professor
Statistics
PHD, Michigan State University, 1980

Rahman, Shahina, Instructional Assistant Professor
Statistics
PHD, Texas A&M University, 2015

Sang, Huiyan, Professor
Statistics
PHD, Duke University, 2008

Schmiediche, Henrik, Instructional Professor
Statistics
PHD, Texas A&M University, 1993

Sinha, Samiran, Professor
Statistics
PHD, University of Florida, 2004

Spiegelman, Clifford H, Distinguished Professor
Statistics
PHD, Northwestern University, 1976

Subbarao, Suhasini T, Professor
Statistics
PHD, University of Bristol, 2001

Vidakovic, Branislav, Professor
Statistics
PHD, Purdue University, 1992

Wang, Suojin, Professor
Statistics
PHD, University of Texas at Austin, 1988

Wang, Tiandong, Assistant Professor
Statistics
PHD, Cornell University, 2019

Wehrly, Thomas E, Senior Professor
Statistics
PHD, University of Wisconsin - Madison, 1976

Wong, Ka Wai, Associate Professor
Statistics
PHD, University California, Davis, 2014

Zhang, Xianyang, Associate Professor
Statistics
PHD, University of Illinois at Urbana - Champaign, 2013

Zhou, Lan, Associate Professor
Statistics
PHD, University of California, Berkeley, 1997

Zhou, Quan, Assistant Professor
Statistics
PHD, Baylor College of Medicine, 2017