Prerequisite: Two years of high school algebra with a grade of C or better. Study techniques used in organizing data, including frequency distributions, histograms, measures of central tendency, measures of dispersion, probability distributions, point estimation, interval estimation and testing hypotheses.
Provides tools to prepare data, critique and improve visualizations of statistical data, learn visual encoding principles of quantitative information, and learn how these principles are applied to create effective visualizations.
Prerequisite:
STAT 150 or equivalent. Study of inferential techniques including nonparametric methods, ANOVA models, experimental design, multiple regression, sampling methods and control charts.
Study of inferential techniques including nonparametic methods, ANOVA models, experimental design, multiple regression, sampling methods and control charts.
Introduces conceptions of statistics, data analysis, and concepts of probability. Focus is on understanding variability and probability, sampling and random variables, descriptive and inferential statistics.
May concurrently take
MATH 132: with minimum grade of C
Topics in multiple linear regression, estimation of model parameters, inferences, diagnostics, model assumptions, ANOVA formulation.
Introduction to elementary sampling concepts. Includes random sampling, stratified sampling, cluster sampling and systematic sampling. Inferences and assumptions are presented for all sampling methods.
This course is an introduction to the elements of data science. Topics include data visualization, data wrangling, statistical learning and predictive analytics, text mining and spatial data.
Individualized investigation under the direct supervision of a faculty member. (Minimum of 37.5 clock hours required per credit hour.)