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Abstract:
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Not typically a course taught at the undergraduate level, we share successes and challenges faced in teaching a Spatial Data Analysis class as an upper-level elective for the Statistics major at Gustavus Adolphus College. With a focus on environmental and biological applications, students learn what it means for data to be correlated and how to analyze both geostatistical and point process data. The course affords students the opportunity to expand their thinking about statistics beyond regression, and to think broadly about the ideas of hypothesis testing and prediction. As this course is an option for the "capstone experience" of the Statistics major, students were given problems to complete in teams each week in lieu of traditional homework, and teams then presented solutions to the class. The course offers a unique blend of advanced statistical ideas, computing, data visualization, teamwork, and communication skills in alignment with ASA guidelines for undergraduate majors. We will present our experience in the development and teaching of the course and provide suggestions for future changes we hope to make.
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