Baylor University
Department of Statistical Science
College of Arts & Sciences

Baylor > Statistical Science > Undergraduate Programs > Course Descriptions

Undergraduate Courses (Undergraduate Catalog page 588)

Prerequisite(s): Freshman standing and consent of statistics undergraduate faculty advisor. Philosophical, ethical, and sociological issues related to statistical uncertainty and randomness.

Introduction to traditional statistical concepts including descriptive statistics, binomial and normal probability models, tests of hypotheses, linear correlation and regression, two-way contingency tables, and one-way analysis of variance. Credit may not be obtained after receiving credit in STA 2381 or 3381.

Prerequisite(s): A grade of C or above in MTH 1321. Parametric statistical methods. Topics range from descriptive statistics through regression and one-way analysis of variance. Applications are typically from biology and medicine. Computer data analysis is required

Prerequisite(s): A grade of C or above in MTH 1322. Introduction to the fundamentals of probability, random variables, discrete and continuous probability distributions, expectations, sampling distributions, estimation and simple tests of hypothesis.

Prerequisite(s): Three hours of statistical methods. Planning, execution, and analysis of sampling from finite populations. Simple random, stratified random, ratio, systematic, cluster, sub sampling, regression estimates, and multi-frame techniques are covered.

Prerequisite(s): STA 3381. Terminology, techniques, and management of Data Mining for biostatisticians.

Prerequisite(s): STA 3381. Data Analysis for biostatisticians in the biomedical and pharmaceutical fields.

Prerequisite(s): STA 3381. Computational methods using statistical packages and programming.

Prerequisite(s): STA 3381 or equivalent. Development of statistical concepts and theory underlying procedures used in statistical process control applications and reliability.

Prerequisite(s): A grade of C or above in either STA 2381 or STA 3381; or consent of instructor. Development and application of two-sample inference, analysis of variance and multiple regression. Assumptions, diagnostics and remedial measures are emphasized. Computer statistics packages are utilized.

Prerequisite(s): A grade of C or above in MTH 2321. Probability theory and mathematical statistics at the post-calculus level. Principal topics include probability axioms, random variable, expectation, central limit theorem, special discrete and continuous distributions, and an introduction to sampling theory and data reduction.

Prerequisite(s): A grade of C or above in STA 4385. Sampling distributions, sufficient statistics, likelihood procedures, point estimation, hypothesis testing, and confidence intervals. Other topics include Bayesian inference, multivariate transformations, and analysis of categorical data.

Prerequisite(s): A grade of C or above in STA 4385. Applications of probability theory to the study of phenomena in such fields as engineering, management science, social and physical sciences, and operations research. Topics include Markov chains, branching processes, Poisson processes, exponential models, and continuous-time Markov chains with applications to queuing systems. Other topics introduced are renewal theory and estimation procedures.

Prerequisite(s): Approval of the statistics undergraduate faculty advisor. Statistical concepts applied to written and oral reports for consulting. For students majoring in statistics.

Prerequisite(s): STA 2381 or STA 3381. Topics in probability and/or statistics not covered in other courses. May be repeated for a maximum of 6 hours if the content is different.