A beginning course in statistics intended for students in a wide variety of fields of study. Course topics include descriptive statistics, normal distribution, correlation and regression, basic concepts of probability, binomial distribution, sampling distribution, confidence intervals and hypothesis testing for mean(s) and proportion(s). Students also have the opportunity to analyze data sets using Statistics software. Prerequisite: Appropriate score on mathematics placement test, advanced placement, or a grade “C” or better in MTH 1105.
This course is designed to provide students with a foundation in statistical methods, including data exploring and strategies in sample surveys, estimation and testing hypotheses of means and variances, analysis of variance, regression analysis, contingency tables. These concepts are taught with heavy emphasis on statistical computing software and real-world datasets. Students will learn basic skills of statistical packages that are widely used in business, industry, government, and research. Prerequisite: A grade of “C” or better in MTH 1112.
Topics include sampling frames, questionnaire design, simple random, systematic, stratified, and cluster sampling, comparing domain means, contingency table analysis. Prerequisite: STAT 2210.
Probability theory, sample spaces, random variables, mutual exclusion, independence, conditional probability, permutations and combinations, common discrete and continuous distributions, expected value, mean, variance, multivariate distributions, covariance, Central Limit Theorem. Prerequisite: MTH 1125 and STAT 2210.
Fundamentals of the theory of statistics, the Central Limit Theorem, point estimation, sufficiency, consistency, hypothesis testing, sampling distributions, confidence intervals, linear regression models, interpretation of experimental results, Bayesian Estimation. Prerequisite: STAT 4451.
The fundamental concepts of financial mathematics and how these concepts are applied in calculating present and accumulated values for various streams of cash flows as a basis for future use in the following: reserving, valuation, pricing, asset/liability management, investment income, capital budgeting, and valuing contingent flows.
Topics include simple linear regression, inferences in regression analysis, techniques of multiple regression and model building, ANOVA as regression analysis, analysis of covariance, model selection and diagnostic checking techniques, nonlinear regression, and logistic regression. Computations are an integral part of the course and will involve the use of SAS and R statistical software. Prerequisite: STAT 3350.
Possible topics to be covered include multivariate descriptive statistics, multivariate normal distribution, analysis of covariance, MANOVA, multivariate regression, principal components, discriminant analysis, cluster analysis, factor analysis. Some familiarity with R and SAS is expected. Prerequisite: STAT 4451, and STAT 3350.
In recent years, the use of specialized statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. The course gives an introduction to analyzing categorical data. Principal topics include: contingency tables, generalized Linear Models, log-linear models, logistic regression, and models for matched pairs. Two statistical software packages SAS and R will be used appropriately throughout the course. Prerequisite: STAT 3350.
Data Mining is a fast growing interdisciplinary field between Statistics and Computer Science due to the emergence of massive dataset. This course covers an information extraction activity whose goal is to discover hidden facts from large data warehouses. A number of data mining tasks including description, classification, selection, estimation, prediction, and affinity grouping and clustering will be discussed. Also, students will learn how to use data mining software to perform data mining functionalities. Prerequisite STAT 3350.
Fundamental principles of designing and analyzing experiments with application are considered. The concepts of experimental unit, randomization, blocking, replication, error reduction and treatment structure are introduced. The design and analysis of completely randomized, randomized complete block, incomplete block, Latin square, split-plot, repeated measures, factorial and fractional designs will be covered. Prerequisite: STAT 3350.