Spatial statistics and experimental design are among the most important topics students in the environmental and ecological sciences learn and utilize throughout their careers. These topics are also among the most difficult for students to learn, often due to the use of contrived data sets that present simplified and unrealistic scenarios that fail to engage students in higher level thinking. One way to engage students in higher level thinking is to use an inquiry-based pedagogical framework. The use of inquiry as a pedagogical approach should be instinctive for most scientists, as it mimics how science is conducted, yet most instructors continue to use lecture-based, textbook-driven instructional formats. This type of approach is efficient in covering material, but it suffers in its ability to engage students or enhance learning. Using a Bigfoot data set in an inquiry-based framework, students in a cross-listed graduate/undergraduate statistics class learned ordinary least squares regression and geographically weighted regression techniques. These techniques are among the most frequently applied analyses in the natural sciences. The use of a Bigfoot data set engaged students’ interest, rendering the prospect of learning regression topics as an emergent property of their interest and engagement. This approach also has an additional benefit in that students learned not only key statistical concepts but also learn how to self-diagnose deficiencies with their models as well as how to identify strategies to overcome these deficiencies. We hope that both instructors and students in graduate and undergraduate statistics or spatial modeling courses find this case study, and included data sets, a useful and interesting approach to teach and learn regression and spatial regression.

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