Essentials in the quantitative analysis of spatial and other data, with a particular emphasis on statistics and programming. Topics include data display, data description and summary, statistical inference and significance tests, analysis of variance, correlation, regression, and some advanced concepts, such as matrix methods, principal component analysis, and spatial statistics. Students will develop expertise in data analysis using advanced statistical software.

Prerequisites/Rules:
Credit only granted for: BIOM301, BMGT230, CCJS200, ECON321, EDMS451, GEOG306, GVPT422, PSYC200, or SOCY201. General Education: FSAR
Credits: 3
Grading Method: Regular

Course Offerings

    Spring 2019Instructor: Naijun ZhouView: Syllabus
    Fall 2017Instructor: Naijun ZhouView: Syllabus
    Summer 2017Instructor: Amanda Hoffman-HallView: Syllabus
    Fall 2016Instructor: Naijun ZhouView: Syllabus
    Fall 2015Instructor: Naijun ZhouView: Syllabus
    Winter 2015Instructor: Amanda Hoffman-HallView: Syllabus
    Fall 2014Instructor: Giovanni BaiocchiView: Syllabus