10 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.
STAT 1000Companion to Statistical Reasoning1
An optional companion course designed to support students concurrently enrolled in STAT 1100, this course will provide support for Statistical Reasoning, including supplementary instruction and just-in-time review of prerequisite concepts. Topics,
A non-theoretical course designed to improve a student's statistical literacy. Topics include: describing distributions; introduction to sampling and study design; creating and interpreting data representations; introduction to probability; statis
A nontheoretical introduction to statistics with an emphasis on applications in a variety of disciplines. Topics include measures of central tendency, position and dispersion; basic probability; hypothesis testing; estimation; analysis of variance
Linear time models, seasonal models, stationary models, moving average, autoregressive and ARIMA models, model identification, confidence intervals and testing, forecasting and error analysis. Prerequisites: (MATH 2472 and STAT 2610) or STAT 3631.
This is a first course in regression analysis with an emphasis on applications. Topics covered include simple and multiple linear regression, hypothesis testing, analysis of residuals, polynomial regression, variable selection and model building, Pre-requisite(s): MATH 3310 and STAT 2610 or consent of instructor
Probability of finite sample spaces, discrete and continuous probability distributions, exploratory data analysis, statistical models. Prerequisites: MATH 2472.
Multivariable distributions, sampling distribution theory, estimation, hypothesis testing, regression and correlation. Prerequisites: MATH 2480 and STAT 3631. (Might not be offered every year.)
Introduction to descriptive and inferential statistics in the context of the health sciences. Covers data types, methods for summarizing and displaying data, measures of central tendency and variability, hypothesis testing including the analysis o
Students design and complete a data science project in conjunction with the course professor. The project requires the student¿s accumulated academic experience to solve a challenging problem. The project will focus on real data sets and students