17 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.
Course not otherwise included in the catalog listing but for which there may be special needs. May be repeated for credit as often as permitted and as subject matter changes. See department chair.
Introduction to the principal statistical methods for investigating the stochastic elements of organization. The focus of the course includes: understanding the methods; selection of methods appropriate to a process; interpretation of results. Major topics include: descriptive statistics, discrete and continuous probability distributions; sampling; statistical inference and regression methods. Computer-based assignments will be used for selected areas. (Students may not receive credit for both STAT 185 and STAT 225.) Fall and Spring.
An introduction to basic concepts of discrete and continuous probability distributions and their applications. This course is only open to students who have credit for STAT 185 but whose major requires STAT 225. (Students may not receive credit for both STAT 225 and STAT 220.)
Organization and display of data; measures of central tendency and dispersion; alternative formulations of probability; distributions of random variables; the Central Limit Theorem; statistical inference, confidence intervals and hypothesis tests; contingency table analysis; analysis of variance; and linear correlation and regression. (Students may not receive credit for both STAT 185 and STAT 225.)
Introduction to statistical software used in data analysis. Activities include but are not limited to visualization, programming, and simulations. Statistics software may include SAS, SPSS, R or similar packages. May be repeated as topics vary for a total of 4 hours credit.
Course not otherwise included in the catalog listing but for which there may be special needs. May be repeated for credit as often as permitted and as subject matter changes. See department chair.
Course focuses on alternative statistical techniques used to explore and analyze data. Content may include, but is not limited to graphical data analysis, assessing normality and transformations, categorical data analysis, nonparametric statistics, topics in simple regression, and introduction to time series analysis.
Estimation and inference for regression models. Topics include: least squares estimation; models comparisons; estimation of validity of model assumptions and remedial measures; simple linear, multiple linear, non-linear and logistic regression; and dummy variables.
Design, estimation and inference for ANOVA and related models. Topics include single factor and multiple factor ANOVA, fractional factorial, split-plot, and repeated measures designs, examination of validity of model assumptions and remedial measures, and analysis of covariance.
The analysis of unstructured and large data sets. Topics include preparing data for deeper analysis, breaking down the process into manageable steps, regression techniques and Bayesian approaches for dealing with multivariate data, and Exploratory Data Analysis with statistics software such as SAS, R, or similar packages.
Course not otherwise included in the catalog listing but for which there may be special needs. May be repeated for credit as often as permitted and as subject matter changes. See department chair.
Various formulations of probability, the structure of probability spaces, combinatorial analysis, discrete and continuous random variables, joint distributions, the Central Limit Theorem, moment generating functions and characteristic functions.
Sampling distributions of statistics, properties of statistics, general principles of statistical inference, linear statistical models, some non-parametric statistics, Bayesian statistics, and an introduction to statistical decision theory.
This course covers core topics in supervised and unsupervised learning. Topics include selecting appropriate models for prediction and classification, implementing rigorous model validation methods, applying optimization criteria, using various regression techniques for predictive analysis, and exploring key unsupervised learning techniques, such as Principal Component Analysis. The course utilizes common statistical software packages such as SAS, R, or similar packages to provide practical, hands-on experience. Prerequisites include one of the following: 1) STAT 185 or 225, and CSCI 182, 183, or 185, and MATH 291 and 365, 2) MATH 295, or 3) permission of instructor. Even years Fall.
This capstone course emphasizes the practical application of data science. Students are expected to conduct a comprehensive study using modern data science techniques. In addition to demonstrating technical proficiency, students develop professional communication skills by presenting their findings in a public presentation. Emphasis is placed on clear, accurate interpretation of methods and results, as well as on the ability to convey the broader significance of the work.
Course not otherwise included in the catalog listing but for which there may be special needs. May be repeated for credit as often as permitted and as subject matter changes. See department chair.
Independent research under the supervision of a faculty mentor. An IP grade may be awarded at discretion of instructor. May be repeated for a total of 6 hours credit. See department chair.
Source: University of North Carolina Asheville's catalog, linked per course · table learning_unit · CourseShelf publish 61
