NR: 140 Applied Environmental Statistics Instructor: Jennifer Pontius Office: 205C Aiken Office Hours: 24/7 Online Discussion Board, Thursdays 11:30 1:00 Aiken 205C Email: Jennifer.pontius@uvm.edu (6-3091) Class Times: Lecture TR 10:05-11:20 Aiken 102 Description This is a one-semester course in biostatistics for students interested in conducting and interpreting statistical analyses for natural resource applications. This is designed as an introductory course, targeted to undergraduate students with little formal training in statistical methods, or for students looking to expand their working knowledge of statistics for common application in natural resource professions. Students will be introduced to concepts such as data interpretation, description and visualization, concepts in distributions and probability, and statistical techniques including: tests (independent & dependent samples), Analysis of variance, Chi-square, correlation, and regression. While we will cover some mathematical theory, the course emphasizes application and interpretation of statistical methods and concepts. We will use natural resource case studies to work with the methodology and demonstrate course concepts. Both MS Excel and JMP Statistical Software will be fully integrated into the course. Course Learning Objectives: At the end of this course students will be able to: Understand the importance of statistical literacy in global citizenship. Critically assess statistics presented to them through the literature / media. Identify the common experimental and sampling designs used in Natural Resource professions. Accurately describe, visualize and summarize data Demonstrate an understanding of distributions, probability and their role in hypothesis testing Master basic statistical analyses (including non-parametric options) o z-tests o T-tests (independent and dependent) o ANOVA (one-way and factorial w interactions) o Correlations o Regression (simple and multiple linear) o Chi-square Identify which analyses are appropriate for given experimental designs and hypothesis. Correctly and thoroughly interpret and communicate statistical results.
Course Format Students will be exposed to materials and concepts each week in a hybrid format of scaffolded assignments. What does this mean?: Through continuous exposure to concepts throughout the week (short assignments of increasing difficulty), available through a combination of in-class and online activities you will have the exposure and practice necessary to achieve statistical competence. What does this hybrid course look like?: A series of online materials (reading, video, web-links) and assessments (online quiz) will be completed prior to class to prepare you for a condensed lecture on Tuesday each week. Lecture will quickly summarize the basic concepts you should have gotten from the online work, with example applications. Thursday class time will be primarily used for in class group work, practicing with new data sets and reporting results and conclusions back to the larger class. After class on Thursday you will be ready to tackle a larger problem set. Online video tutorials will provide guided examples of analyses and interpretation each week. An Example Weekly Module: (Sun / Mon) Online Independent Prep (required completion prior to lecture): Read Tools Evaluation Statistical Spin.pdf Study Questions to Critique Stats.pdf Watch TED Talk by Hans Rosling Complete and Submit Online quiz (Tues)F2F: lecture session Content review via condensed lecture and examples (Thurs)F2F: group work and practice session Guided group practice exercises Group submission of solutions (Fri / Sat) Independent Online Work: Online Tutorials are provided Problem Set submission by Sunday 12PM.
This sounds like a lot of work! But you have to trust that it is ONLY through being hit over the head with statistics over and over, and practicing them ad nausea, that you will become statistically literate..a skill that all natural resource administrators require of new hires. The hybrid approach allows us flexibility in our personal schedules to be able to fit all of this work into weekly routines. While the class is 4 credits, we will only need to meet in class for two- fifty minute sessions. The rest can be done anytime, anywhere (pajamas optional). With this many assignments (readings, quizzes, problem sets, group work, exams), with so many different formats (online, in class, group work, blackboard uploads, etc.), this could get complicated. That is why each week all assignments will be clearly laid out for you in weekly learning modules on blackboard. Textbook: We will be using the first half of the textbook Science and the Global Environment. This Tools and Skills section of the text summarizes many of the quantitative skills we will be covering in this class in a clear, concise format. This text is available online through our UVM library. To access the text go to the UVM library home page: http://library.uvm.edu/ Search the holdings for Science and the Global Environment Click on the title Login in (for electronic access) through your library using your UVM id and password Click the Read Online (Available) tab This leads you to the table of contents for further navigation Chapters will also be linked via blackboard but access will require your login. Note that once in, you should be able to print out pdf copies. If you prefer a hard copy you can also order the text, but this is not necessary. Supplies: Access to a computer with some internet browser for access to Blackboard and Microsoft Excel or similar spreadsheet software. You will also need access to the statistical package JMP from the SAS Institute. These packages are available on all university computers, including the ones in the Aiken Computer lab. If you would like, you can download JMP to your personal computer from our UVM software portal (you will need to log in, select your operating system and then download, unzip and install JMP v 12 Pro): https://www.uvm.edu/software/ If you have not used these software packages before, do not panic. We will be using these in class to demonstrate methods (and they were selected because of their statistical power AND user friendliness). Someone in your small group will need a laptop with these software packages for in class exercises on Thursdays. If no one in your assigned group has access to a laptop, let me know the first week of class so we can mix up groups.
Blackboard: This course is structured entirely in blackboard. Your weekly learning modules will outline what pre-lecture activities are required, links to materials for in-class and problem set completion, submission of all assignments, as well as exam access and submission. You will have group work space for online discussion board and communications with other students in your small group. This is a great way to plan and complete out of class activities (even remotely if desired), as well as support each other (ask/answer questions) in preparing for class or studying for exams. Grades will be tracked in blackboard so that you can keep up with your status as the course progresses. If you notice any discrepancies in your grades, please contact me. Course Assistance: As we progress through the course, there are several ways for you to get additional help: (1) Each week you will have an opportunity to specify what about a given topic is unclear or confusing using the module discussion boards. We will use this feedback to guide how we spend our in class time. (2) Ask questions during class! If you are confused you are likely not alone. (3) Use your small group. You are assigned to a support group specifically to ask each other questions (e.g. what the heck did she mean by.. in lecture today? ) and help each other work through assignments. You can decide to have a regular in person group meeting time (you pick what works for your schedules, or post your question to your group discussion board on blackboard. (4) Post a question to the class discussion board for ongoing feedback. (5) Make an appointment with me. I am available to meet with you one on one. Just email or call to set up a time. Assignments: Assignments and exams are used to enhance your learning experience in this course. You cannot become proficient in statistics without DOING statistics. You will have several opportunities to practice before you are tested on them. This includes: Independent Online Quizzes: Over the course of the semester, I will post weekly quizzes on Blackboard. These will be open book / open notes quizzes that are designed to: 1) help identify the key concepts and information from videos, tutorials and readings you have completed to prepare for lecture, 2) Provide an incentive to keep you up to date (and avoid last minute cramming of overwhelming amounts of information the night before an exam), and 3) evaluate where students have common problems to guide what we cover in class. These must be submitted before Monday at midnight so that I can look through for common problems. (Worth 1 pt each = 15% total grade) In class Group Exercises: Each Thursday in class we will have practice exercises to work through in our small groups. Participation in these exercises is vetted by submitting your group answers via a blackboard discussion board. Each participating group member will be given credit for successful completion of these activities at the end of class. (Completion is worth 1 point each as a group grade = 9% total grade)
Problem Sets: Problem sets are formal homework assignments that will be worked on independently and submitted via blackboard for grading. These typically include a pdf guide with questions, datasets to download, a tutorial video and an online submission portal where your answers are entered for grading. Problem sets are due on midnight Sunday of each week they are listed. Problem sets are designed to be completed outside of class time. Understand that these problem sets are crucial preparation for exams. While I do allow students to work together on these, it is in your best interest to work in parallel and use your peers only when you are stuck. Exams: Two exams will be taken in the Aiken Computer lab so that you have access to the software you may need. These online evaluations will cover concepts and theory discussed in class but will primarily focus on exercises and analyses practiced in problem sets. (18 pts each = 36% total grade). Grading: 35 % Exams (2 at 18.5 points each) 14% Online Quizzes (14 at 1 point each) 9% (In class Group Work) Class Exercises (9 at 1 pt each) 40 % Problem Sets (10 at 4 points each) Late Policy Late assignments will be immediately reduced 10%, and an additional 10% for each additional week late. Assignments will no longer be accepted after 3 weeks (without prior instructor approval). Honor Policy I strongly support the UVM honor system. While I expect you to do your homework individually, you may work in collaboration with another student. If the work is done collaboratively, both names should appear on the paper, and I assume that both students fully understand the work that was done. Unless otherwise instructed, exams will be individual efforts. I urge you to review UVM's Academic Honesty Policy as it appears on the UVM web page. And, if you have any questions, please discuss them with me. http://www.uvm.edu/~dosa/handbook/?page=academic.html
Tentative Schedule Week Pre-lecture quiz (due Monday by midnight) Tuesday Thursday Problem Sets (due Sunday by midnight) 1 Jan 17 19 Quiz: Intro Stats Course Intro and Format Terminology and Software Software Tutorials 2 Jan24, 26 Quiz: Spin City Surviving Statistical Spin Group work: "bad stats" PS: Spotting Spin 3 Feb 31, 2 Quiz: Experimental Design and Sampling Experimental Design and Sampling Techniques Group work: experimental design PS: Sampling and Design 4 Feb 7, 9 Quiz: Descriptive Statistics Descriptive Statistics Distributions and Normality PS: Descriptive Stats 5 Feb 14, 16 Quiz Distributions, Probabilities and Hypothesis Testing Probability and Hypothesis Testing Group work: Describing data and distributions PS: Probability and Hypothesis Testing 6 Feb 21, 23 Quiz: z-tests One Sample z-test Group work: z-tests PS: z-tests Week 7 Feb 28, 2 Study Study Study Group independent study Exam 1 8 Mar 7, 9 Quiz: t-tests Independent t-tests t-tests II Spring Break 9 Mar 21, 23 10 Mar 28, 30 Quiz: t-tests Dependent t-tests Group work: t-tests PS: t-tests Quiz: ANOVA ANOVA ANOVA (Factorial) 11 Apr 4, 6 Quiz: Factorial ANOVA Group work: ANOVA PS: ANOVA 12 Apr 11, 13 Quiz: Correlations Correlations Group work: Correlations PS: Correlations 13 Apr 18, 20 Quiz: Regression Linear Regression Mutiple Linear Regression 14 Apr 25, 27 Quiz: Multiple Regression Group work: Regression PS: Regression 15 May 2, 4 Final Exam Week Quiz: Chi sq. chi-square Group Work: Chi-square PS: Chi-square Exam 2