GEOG 3023 Statistics and Geographic Data
About the Course:
In this course, students will discover application of statistical methods to geographic research, including sampling, distribution measurement, characterizing relationships, multivariate analysis, and a strong focus on computational modeling statistical relationships using R. The course covers the foundations of statistics with a strong emphasis on constructing models from data. Topics include exploratory data analysis, descriptive statistics, probability, multiple regression, analysis of variance, logistic regression, and spatial statistics. The students will use the R statistical programming language to conduct computational statistical analyses. This is a powerful software tool with a strong user community, plenty of excellent examples, and a syntax for modeling that is both intuitive and powerful. In addition to helping students develop strong statistical know-how, this course will prepare students for more advanced spatial analysis topics covered in courses such as GEOG 4023/5023. Students will learn the context and application of these concepts and techniques. Students will acquire the capacity for creative and critical thinking in the application and analysis of statistical methods and results.(From Course Catalog)Introduces parametric and distribution-free statistics, emphasizing applications to earth science problems. Same as GEOL 3023.
Course Prerequisites: List course prereqs here: No formal Prerequisites required by the department. The course serves as a prerequisite to other Geography Department courses, however.
Proctoring (if applicable): N/A Course is designed for final project, no final exam.
By the end of the course you should be able to: perform basic to intermediate statistical analysis using R code,understand basic to intermediate statistical theory, interpret the results of basic to intermediate statistical methods, and design and execute proper statistical analysis of spatial and non-spatial data.
I use an open source test book which is better than any of the for purchase ones I could find. More important here is ensuring access to a computer that meets minimum computing requirements for R use, and for students to have the ability to store data on that computer to do labs.
Grading (out of n points):
Lab assignments 40% : The labs are designed as practical exercises you can use as a compliment to the lectures. The Labs feature a self paced tutorial which will walk you through everything regarding that week’s lab topic. Using the techniques explained in the tutorial, you will then complete an assignment which requires you to demonstrate these skills on your own. The techniques learned in the labs will be indispensable to your ability to design and complete your final project.
Quizes 20%: Quizzes are short series of questions relating to the lecure, reading, and/or lab concepts with the same overall theme.
Discussion Posts 10% To remain engaged, and to offer another format for exploring lecture, reading, and/or lab concepts in an open context complimentary to quizzes, guided discussion posts will be required throughout the course.
Final Project 30% Students will complete a well-researched, designed, and executed project using the techniques learned in the class. Projects can be related to problems and areas of inquiry particular to the student’s own interests. Students will propose a problem with a geographic context, and submit a project proposal. Students will identify and collect data, and perform the statistical analysis in R. Students will create a report in r markdown, and export it to html for submission. Details, including the rubric, for the final project will be posted on Canvas.