Course Syllabus
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Areas of Interests and Expertise
Awards and Achievements
Phone: + 1 405 325 4319 Email: jgreene@ou.edu Instructor Live Session : Thursday 6:00-7:00 p.m.(Central) Office Hours Tuesday and Thursday 9:00-10:00 Monday 5:00-6:00 p.m. by appointment (review on lecture and book HW) Zoom link: https://oklahoma.zoom.us/j/4197934126?pwd=cEJucEhneERPYWFFd2VVUmtRV05nUT09 Password: 85536639 |
Professor and Chair Department of Geography and Environmental Sustainability |
Graduate Teaching Assistant Contact Information:
Eric Bump
Email: eric.bump@ou.edu
TA Office Hour: Thursday 7:00-8:00 PM (Central)
(Computer based Homework review)
Zoom link: https://oklahoma.zoom.us/j/7830871383?pwd=R3VSUE9iMVZMdUh1UGZTblpPTERYQT09
Meeting ID: 783 087 1383
Passcode: 11032018
Assumed Prior Knowledge
There are no prerequisites for the course. We will be examining statistical processes to determine how numbers and data can be used to identify patterns and facilitate decision-making. To accomplish this, you will be exposed to a series of mathematical techniques and computer software. However, there is no prerequisite that you are familiar with either advanced mathematics or computer programming, as all required skills will be taught as part of the class.
Course Materials
The textbook for the course is:
- Statistical Reasoning for Everyday Life, Bennett, Briggs, and Triola, 5th edition.
In addition to purchasing the textbook, you will also need to purchase the associated online support materials (http://www.pearson.com/mylab). Reading assignments, homework, and additional material will be located through that platform in addition to the materials posted on canvas.
You will also need to use the R programming language. You can find information on how to use and download R and R studio from http://www.r-project.org and http://www.rstudio.com.
Lectures will be based upon concepts from these materials supplemented with other materials provided via Canvas or online resources. Reading the book will help you prepare to fully participate in class discussions and in your written work. Additional information will be posted on the class link at http://canvas.ou.edu.
For a list of course activities, scroll to the bottom of this page.
Grades
Breakdown
The final grade will be calculated based on the following points:
| Class Participation | 160 points (10 points per week; consists of discussion posts and live session engagement) |
| Homework / Quizzes | 375 points (25 points per week; consists of book-based and computer-based assignments) |
| Term Project | 215 points |
| Final Exam | 250 points (2 parts at 125 points each) |
Scale
Points necessary to earn a given final grade will be no higher than the following (i.e., a curve may be applied at the end of the semester to raise grades, but not to lower them):
| A | 900 |
| B | 800 |
| C | 700 |
| D | 600 |
| F | < 600 |
Course Components
Participation
Students should be prepared to participate actively in discussions. Class participation will depend upon regular participation in and contribution to discussion boards and during the live sessions (including an ability to identify and present significant points of readings and discuss the applied material using an informed and researched argument). Participation in the discussion boards and live sessions is a key component of your grade, collectively accounting for your weekly participation grade (50% each). There will be new discussion content each week, and participation will be worth 10 points (160 points total). Students are expected to actively participate in the live sessions and contribute to the discussion boards. If you are unable to attend a live session, you should review the recorded video and prepare a brief summary to ensure you stay engaged with the course content.
Homework / Quizzes
Homework assignments will be associated with new content each week. Students should complete the assigned readings and view the posted videos prior to completing the homework assignment. Each homework assignment will be completed online and is worth 20 points. There will be an assignment each week, with the exception of the last week in the semester. Homework assignments must be completed prior to 11:59 PM (Central) on Sunday of the weeks they are due. Late submissions will receive a deduction of 50% if they are turned in no later than one week late.
Term Project
The term project will consist of a statistical analysis of data. The dataset can be either one of the sample data sets posted on Canvas or a dataset of your own with prior approval.
Each term Project will have the following sections:
- Abstract
A short (no more than 250 words) summary of your data and the key findings.
- Introduction
Three pages (or so) providing some context and background for your data and analysis.
This should include a brief summary/justification for the significance of your dataset upon the scientific literature (e.g., you need at least 4-5 refereed scientific sources providing some scientific context for your work).
- Data
A one page summary of your dataset.
- Results and Analysis
This will include your summary of your statistical summaries and analysis. This will be a subset of the work you actually completed and is intended to show the interesting highlights of the results you have found. You will need to include descriptive and inferential statistics. There is no set number of pages of graphs, but I will expect to see at least 10-15 summaries to inferential tests as well as descriptive and qualitative analyses. You should expect to perform at least 30 hypothesis tests and include the results from the 10-15 of them that you find most statistically significant. This must include examples all of the tests that we discussed in class (e.g., one sample T-test, independent sample T-test, paired T-test, Chi sq test, ANOVA, multiple regression and proportional tests) as well as the descriptive analyses we reviewed this semester (box plots, bar charts, etc.). This section will consist mostly of graphs, charts, tests, etc., so you may have 15 pages of graphs, etc., and only a few pages of text.
- Conclusions/Summary
One page highlighting your key findings and providing an overall summary regarding lessons learned and how you might do things differently if you had the opportunity to do things over, and you how might proceed if you were to expand on the project in the future.
- Bibliography
A listing of all of the refereed materials you cited in the paper. Please use standard APA formatting.
NOTE: While you can work together to collect, process, understand, and analyze the data, each person must turn in his/her own report. Thus, the types and number of graphs, types, and the number of statistical tests, a summary of qualitative analysis, etc. must be different for each individual.
NOTE: Try to use as many different types of statistical tests, charts, and analysis as you can to illustrate that you understand and use the range of techniques we have learned this semester.
Students will post projects to the class website in PDF format by the due date specified in this syllabus, subject to revision notices posted on the class website. Scores for late work submitted no later than one week late will be reduced by 50% unless prior arrangement with confirmation has been made with the instructor.
Exams
There will be a final comprehensive exam at the end of the semester. The exam will consist of two parts, a comprehensive exam covering material from the book, and a computer-based exam in which you will be required to perform different types of statistical analysis. The final online Zoom class will be devoted to reviewing for the exam; students should come prepared with questions to address during the discussion. The book-based exam will be available starting at noon on the final Friday of the semester and will be due that Sunday at 11:59 PM (Central Time). Students will have two hours to complete the book-based exam and only one attempt. The exam is open-book, but preparation is essential in being able to complete the exam in the allotted time.
Course Policies
Communication
The best way to reach me is via email to jgreene@ou.edu. I will respond within 24 hours. Since most homework assignments are due Sunday evening, I will check my email Sunday afternoon for any last-minute help. However, for questions related to an upcoming exam or assignment due date, do not wait until right before the deadline to email me, as there is usually a higher volume of email from this and other courses I teach. You can also call me at 405-325-4319, although I will not respond as quickly as email.
Late Policy
Assignments turned in after due dates will receive a deduction of 50%, unless prior arrangement has been made with me. If an assignment is missed, it must be turned in within one week of the deadline to receive partial credit. No extensions are granted for exams without prior arrangement with me.
University Academic Policies and Student Support
Course Summary:
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