Recent SemestersSpring 2027, Fall 2026Class SizeNot publishedCredits4PrerequisiteCISD 357 or CISP 357 with a grade of "C" or better Transfers ToCSUDescriptionThis course covers principles of descriptive statistics, statistical programming (e.g., R, SAS), statistical modeling, hypothesis tests, confidence intervals, analysis of variance, regression, and categorical data analysis. Students will explore and summarize data, apply multiple comparison techniques in analysis of variance (ANOVA), use chi-square statistics to detect associations among categorical variables, and fit multiple logistic regression models. Emphasis is on fitting models, verifying the model assumptions, using alternative analysis strategies when necessary, and applications to data science. Credit may be earned for either CISD 358 or CISP 358, but not both. 