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This course introduces advanced statistical methods used in information systems and information science & technology (IS&T) research and practice. It is designed for IS/MIS majors seeking to develop the quantitative and analytical skills required for data-driven decision making, applied research, and advanced study in information systems, computer science, and information technology. The course emphasizes the application, evaluation, and interpretation of statistical models rather than mechanical computation alone. Students learn how to select appropriate statistical techniques, assess underlying assumptions, analyze real-world data, and draw valid conclusions in applied settings. Throughout the course, students use statistical software packages (e.g., R) to conduct reproducible analyses and interpret model outputs. Topics include experimental design, analysis of variance, regression modeling, time series analysis, and nonparametric methods. By the end of the course, students will be able to independently conduct advanced statistical analyses and critically interpret results in IS&T contexts. (Cross-listed with ISQA 8156)

Prerequisite(s): CIST 2500 or equivalent (at least one course in statistics) with a grade of C- or better.