16 courses with the subject STAT, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Any MATH class, or a passing score on a placement test approved by the Department of Mathematics and Statistics Designed for students who need to comprehend statistics that is used in the media, print, and to a lesser extent peer reviewed journal articles. The aim of this course is to explore the way statistics are used in society. Emphasis is placed on understanding descriptive and inferential statistics as they are presented in various media and print venues. REAL Area R
Any MATH class, or a passing score on a placement test approved by the Department of Mathematics and Statistics Introduction to statistical methods; descriptive statistics, normal distribution, estimation, hypothesis testing, correlation and regression. REAL Area R
Any MATH class, or a passing score on a placement test approved by the Department of Mathematics and Statistics An introduction to statistical methods taught by embedding statistical language and methods into a biological context. Topics will include descriptive statistics (univariate and bivariate graphs; measures of central tendency, dispersion, and position); probability distributions; estimation; hypothesis testing; correlation; regression. REAL Area R
Permission of instructor. Special topics in statistics that are accessible to non-mathematics majors, as student and faculty interest demands. Syllabus is available each time the class is offered. Interested students should contact the department chairperson or the course instructor before registering.
STAT 301Foundations of Probability and Statistics4
MATH 172 Introduction to probability and statistical inference. This course covers probability rules, common probability distributions, and one-sample confidence intervals and hypothesis tests for population mean and proportion. REAL Area R
STAT 301 Study of statistical methods with emphasis on inference, applications, and computing. Topics include two-sample hypothesis tests and confidence intervals, analysis of variance (ANOVA), correlation and simple linear regression, Chi-squared tests, and an introduction to nonparametric methods. Statistical computing using modern software for data analysis (such as R) is integrated throughout the course. REAL Area L
STAT 200 . Students will focus on common statistical practices in biology, public health, and medicine. This course provides students with the conceptual knowledge to evaluate statistical results as well as practical knowledge to analyze data. Statistical analyses of bivariate and multivariate data are covered. Further examination of analyses covered in STAT 200 is provided, as well as topics such as logistic regression, survival analysis, sample size and power analysis, and additional analyses relevant to case-control and cohort studies.
STAT 200 or STAT 301 . Examines distribution-free analogs of many classical statistical tests. Topics include tests based on binomial distribution, tests based on Fisher’s method of randomization, goodness of fit tests, two sample tests, and correlation procedures. Modern computer software will be used to analyze real world data.
STAT 200 or STAT 301 Introduction to programming in R for exploratory data analysis and statistical inference. This course covers the basics of R programming, data manipulation, graphics, probability distributions, confidence intervals, and hypothesis testing.
STAT 302 This course introduces the fundamental concepts of regression methodology and data analysis using statistical software packages, such as R and SAS. It covers the basics of linear regression models, least squares estimation, model selection and validation, and model diagnostics. Emphasis is placed on analyzing data using modern software tools and interpreting results in the context of real-world applications.
STAT 302 . This course introduces different types of statistical experimental designs that commonly arise in engineering, epidemiology, medicine, pharmacology, and other applied areas. The experimental designs will be covered in the context of real-world applications and the statistical models will fall under the framework of linear regression. Statistical software packages, such as R and SAS, will be used throughout the course for implementing the statistical models. REAL Area L
STAT 302 A modern statistics course for data science with emphasis on computation, inference, and reproducibility. Core topics include simulation and resampling, generalized linear models, penalized regression (ridge, lasso), computational inference methods, and Bayesian approaches. Students use a statistical programming language such as R or a similar platform for analysis, visualization, and reproducible workflows, with applications drawn from real-world datasets. Ethical and inclusive data practices are emphasized throughout.
STAT 480Advanced Topics in Statistics1-6
Subject
STAT
Credits (min)
1
Credits (max)
6
Credit unit
Credits
Type
course
Repeatable
May be repeated for credit with a different topic.
Major in mathematics, enrollment in the Honors Academy, completion of all other Honors Academy requirements, a minimum 3.5 GPA in all courses and in mathematics and statistics, senior standing. Topics in statistics determined by the student, the faculty member with whom the student works and the department. In order to receive honors credit, a student must earn a grade of A or B for the final project. See “ Honors College .”
Junior or senior standing, at least a 2.5 GPA overall, at least a 2.5 GPA in mathematics and statistics and permission of instructor. Applications of theory learned in the classroom to real-world statistical problems in a professional setting. Provides a platform for building teamwork skills and solving interdisciplinary problems.
