Course Descriptions
STA 220 Probability and Statistics for Science I (3 credits)
Students from the science and engineering programs are introduced to the basics of probability and statistics concepts. This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to real-world problems. A statistical package such as Minitab, R, or other is used for data analysis and statistical applications.
Prerequisite: MAT 212
STA 230 Probability and Statistics for Science II ( 3 credits)
This course covers basic statistical concepts to analyze and synthesize data. Topics covered include: sampling theory, hypothesis testing, confidence intervals, point estimation, and simple correlation, non-parametric testing methods, analysis of variance and covariance, and linear regression. The statistical software package Minitab, R, or other will be used for data analysis and statistical applications.
Prerequisite: STA 220
STA 310 Regression Analysis (3 credits)
This course covers regression techniques with applications to the type of problems encountered in real-world situations. It includes use of statistical software SPSS. Topics include review of simple linear regression, residual analysis, multiple regression, matrix approach to regression, model selection procedures, and various other models as time permits.
Prerequisite: STA 230
STA 320 Design of Experiments (3 credits)
Students in this course study the design and analysis of experiments. It includes extensive use of statistical software. Topics include: single-factor analysis of variance, multiple comparisons and model validation, multifactor factorial designs, fixed, random and mixed models, expected mean square calculations, confounding, randomized block designs, and other designs, and topics as time permits. SPSS software will be used.
Prerequisite: STA 230
STA 340 Statistics for Technology (3 credits)
This course in Statistics for Technology presents the range of statistical methods commonly used in science, social science, and engineering. Topics include: methods of summarizing data, role of computers in analysis, the planning and procedures of experiments, quality control, and life testing. Students are guided through literature searching and report writing. Cases are reviewed from scientific journals for critical analysis.
Prerequisite: Consent of advisor
STA 350 Statistical Analysis for Bioinformatics (3 credits)
This course provides the basics to use probabilistic models and statistical techniques in computational molecular biology. Probabilistic and/or statistical techniques will be presented for the understanding of pairwise and multiple sequence alignment methods, gene and protein classification methods, and phylogenetic tree construction.
Prerequisite: STA 220, consent of advisor
STA 360 Statistical Quality Control (3 credits)
This course provides students with a popular application of statistics in real life industry. Total quality control techniques are highly used in industry and other commercial sectors. It presents the probability models associated with control charts, control charts for continuous and discrete data, interpretation of control charts, and some standard sampling plans as applied to quality control. A statistical software package will be used for data analysis.
Prerequisite: STA 220
STA 370 Introduction to Time Series (3 credits)
This course is a study of the modeling and forecasting of time series. Students are familiarized with non-parametric methods of analysis for a variety of situations. Moreover, students will be trained to compare non-parametric procedures with their parametric counterparts. Topics include ARMA and ARIMA models, autocorrelation function, partial autocorrelation function, de-trending, residual analysis, graphical methods, and diagnostics. A statistical software package is used for data analysis.
Prerequisite: MAT 230 or STA 230
STA 380 Nonparametric Statistics (3 credits)
This course is an in-depth study of inferential techniques and tools that are valid under a wide range of shapes for the population distribution. Topics include: tests based on the binomial distribution, contingency tables, statistical inferences based on ranks, runs tests, and randomization methods. A statistical software package is used for data analysis.
Prerequisite: MAT 230 or STA 230
STA 390 Survey Sampling (3 credits)
This course provides students with the basis for understanding the selection of the appropriate tools and techniques for analyzing survey data and conduct multivariate analysis. Topics covered include: design of sample surveys, methods of data collection, a study of standard sampling methods. A statistical software package is used for data analysis.
Prerequisite: MAT 230 or STA 230
STA 410 Mathematical Statistics I (3 credits)
This course offers students the skills to study statistics from a mathematical standpoint, using probability theory as well as other branches of mathematics such as linear algebra and analysis. This course provides a brief review of basic probability concepts and distribution theory. It covers mathematical properties of distributions needed for statistical inference.
Prerequisite: STA 230
STA 420 Mathematical Statistics II (3 credits)
This course is a continuation of STA 410 covering classical and Bayesian methods in estimation theory, chi-square test, Neyman-Pearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference.
Prerequisite: STA 410
STA 450 Multivariate Statistical Analysis (3 credits)
Students in this course study the theory and practice of multivariate normal distribution, statistical inference on multivariate data, multivariate analysis of covariance, canonical correlation, principal component analysis, and cluster analysis. A statistical software package such as Excel or SPSS is used for data analysis.
Prerequisite: STA 310
STA 460 Statistical Linear Models (3 credits)
This course introduces the theory of linear models. Topics covered include: least squares estimators and their properties, matrix formulation of linear regression theory, random vectors and random matrices, the normal distribution model and the Gauss-Markov theorem, variability and sums of squares, distribution theory, the general linear hypothesis test, confidence intervals, confidence regions, correlations among regressor variables, ANOVA models, geometric aspects of linear regression, and less than full rank models.
Prerequisite: STA 310
STA 490 Special Topics in Applied Statistics (1-3 credits)
This course focuses on real-life practices and challenges of translating theoretical principles into practical applications. The course primarily uses case studies of real-world cases to simulate the managerial and technical challenges that will confront students in the field. This course will cover a wide range of topics to manage risk and uncertainty.
Prerequisite: Senior Standing
STA 492 Seminar in Applied Statistics (3 credits)
Developed to introduce the most recent best practices in the field of Applied Statistics. Students are coached and mentored to use critical thinking and problem solving techniques. Case studies are used.
Prerequisites: Consent of Advisor
STA 497 Practical Training (3 credits)
Students in their junior year are required to work on part time or full time basis in order to experiment with and practice what they learned in class. A student presents a formal report by the end of this training period then he/she makes a public presentation exposing his/her experience.
Prerequisite: Junior standing and Consent of Advisor
STA 499 Capstone Project (3 credits)
Students will utilize the blue prints prepared in the curriculum to deal with statistical situations not encountered in regular course of study. It integrates and synthesizes concepts in statistical theory with applications. Topics include open-ended analysis of data, review of statistical literature on current techniques and practice of statistics, development of statistical communication skills and the use of statistical software tools in data analysis. Each student is required to learn and use a statistical technique beyond what is covered in the previous courses. Students are expected to introduce the method in a presentation and to prepare a comprehensive, professional report detailing the statistical method and its application to a data set.
Prerequisite: Senior Standing
MAT 215 Multivariable and Vector Calculus (3 credits)
This course is principally a study of the calculus of functions of two or more variables, but also includes a study of vectors, vector-valued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, Stokes’ Theorem, Green’s Theorem, the Divergence Theorem, and applications in physics.
Prerequisite: MAT 212
MAT 225 Probability & Statistics for Science (3 credits)
Students from the sciences and engineering programs are introduced to the basics of probability and statistics concepts. Students will cover the concepts, applications and techniques to solve related problems. Contents include probability theory, laws, models, and applications, density functions, statistical analysis using Chi-square testing, t- and f- distributions, estimation, confidence limits, significance tests, and regression analysis.
Prerequisite: MAT 212
MAT 222 Multivariable Calculus (3 credits)
This course is principally a study of the calculus of functions of two or more variables, but also includes a study of vectors, vector-valued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, and includes applications in physics.
Prerequisite: MAT 212
MAT 230 Probability and Statistics for Science II (3 credits)
This course covers basic statistical concepts to analyze and synthesize data. Topics covered include: sampling theory, hypothesis testing, confidence intervals, point estimation, and simple correlation, non-parametric testing methods, analysis of variance and covariance, and linear regression. The statistical software package Minitab, R, or other will be used for data analysis and statistical applications.
Prerequisite: MAT 220
MAT 250 Discrete Mathematics (3 credits)
This course provides preparation for professions that use university mathematics beyond the introductory level. Topics include propositional logic, induction and recursion, number theory, set theory, relations and functions, graphs and trees, and permutations and combinations.
Prerequisite: MAT 212
MAT 300 Actuarial Mathematics (3 credits)
This course prepares students to deal with challenging problems in probability whose solutions require a combination of skills that are acquired in the curriculum. Course work synthesizes basic, essential problem-solving ideas and techniques as they apply to actuarial mathematics.
Prerequisite: MAT 220
MAT 320 Linear Algebra (3 credits)
This course acquaints students with the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, Cramer’s rule, vector spaces, linear independence, basis, null space, row and column spaces of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course.
Prerequisite: MAT 212
MAT 330 Topics in the Mathematics of Finance (3 credits)
This course examines concepts in finance from a mathematical viewpoint. It includes topics such as the Black-Scholes model, financial derivatives, the binomial model, and an introduction to stochastic calculus. Although the course is mathematical in nature, only a background in calculus (including Taylor series) and basic probability is assumed; other mathematical concepts and numerical methods are introduced as needed.
Prerequisite: MAT 215
MAT 400 Stochastic Processes (3 credits)
This course explores Poisson processes and Markov chains with an emphasis on applications. Extensive use is made of conditional probability and conditional expectation. Other topics covered include: renewal processes, Brownian motion, queuing models, and reliability. Students are introduced to a variety of techniques that are widely used in many industries to understand variation, improve product quality, and reduce costs. SPSS will be used.
Prerequisite: MAT 220 or STA 220