class: center, middle, inverse, title-slide .title[ # LECTURE 01: Course Overview ] .subtitle[ ## ENVS475: Exp. Design and Analysis ] .author[ ### <br/><br/><br/>Spring 2025 ] --- <style type="text/css"> body p { color: #000000; } ul { color: #000000; } </style> ## Logistics **Lecture**: Monday, Wednesday, Friday 3-3:50 PM, WS 245 **Credits**: 3 **Instructor**: Dr. Justin Pomeranz [jpomeranz@coloradomesa.edu](jpomeranz@coloradomesa.edu) **Office**: WS 230E, * MWF 2-2:50 pm * Tuesday Morning: Zoom, by appointment. --- ## About me * Grew up in Leadville, CO * Red Rocks Community College + AS Biology * Colorado State University + BS Biology + MS Ecology * University of Canterbury, New Zealand + PhD Ecology --- ## About me * Undergrad: Hated math, wanted to work outside * PhD: math intensive thesis * Taught myself R and programming * Fell in love with it * I hope you do too! * Even if you don't, the skills in this course will benefit your future career, regardless of what you do --- ## About you * Name * Year (Junior, Senior, other?) + I assume your an ENVS major, but let me know if not! * Favorite song or animal (or both) * What you want to do next (Grad school, job, \#vanlife) --- ## Course schedule and materials **Texts** (Available for free): * *The New Statistics with R* by Andy Hector. + Available through the Library * *R for Data Science* by Hadley Wickham & Garrett Grolemund + Available for free online: [https://r4ds.had.co.nz/index.html](https://r4ds.had.co.nz/index.html) --- ## Course Website All learning materials on website * Brief tour <https://jpomz.github.io/ENVS475/> --- ## Course Objectives: *Applied* Statistics **Objectives**: * Practical information on designing experiments and analyzing data * **Analysis Life Cycle** + Research Question + **Study Design** + Data entry, management + **Data Visualization** + **Statistical Analyses and interpretation** --- ## Program Student Learning Outcomes: 1. Design an environmental study. (Quantitative skills, critical thinking skills) 2. Work with data to visualize, analyze, and interpret the results. (Quantitative skills, critical thinking skills) --- ## Software * Course taught in R * No prior experience needed * R is an incredible skill to develop * I'm here to help you do that * No test or trick questions here. If you don't know how to do something in R, **ask me** and I will do everything I can to help! * If using your own computer, ensure that R and R Studio are downloaded before next class + See instructions on website --- ## Why R? * R is free and open source * Runs on all platforms (Windows, Mac, Linux, etc.) * Promotes "Open Science" + e.g., combats the ["replication crisis"](https://en.wikipedia.org/wiki/Replication_crisis) * Widely used in Env Sci, Ecology, etc. + e.g., Grad School! * R is an in-demand skill used across industries + Search for "R Programmer" on Indeed.com * It's 2023, knowing some computer programming will help --- ## General Course Structure **Course mantra**: work hard, learn a game-changing tool for your research, and have an incredibly in-demand skill to help land you a job! This course broadly follows the I do, we do, you do methodology. *Generally*: * Monday - Lecture on topic (I do) * Wednesday - "Lab" on topic (We do) * Friday - Open to work on assignments (You do) --- ## Course Grade Weekly homework assignments (60% of grade) Two exams (40% of grade) * Take-home, open-note format * You will have 1-2 weeks to complete exam * Class time will be provided to work on it and ask questions --- ## Homework * There will be 14 assignments (16 weeks - 2 exam weeks). * Assignments are due Friday at 5 pm + Automatic 2 day grace period, afterwards - 50% + Assignments will not be accepted after 5 days (Wednesday) passed due date * Solutions to assignments will be posted on D2L and/or will be gone over in class. --- ## Homework Grading Rubric Produces the correct answer using the requested approach: 100% Generally uses the right approach, but a minor mistake results in an incorrect answer: 90% Attempts to solve the problem and makes some progress using the core concept: 75% Incomplete assignment submitted; OR complete assignment submitted after 2-day grace period: 50% Answer demonstrates a lack of understanding of the core concept; OR mostly incomplete: 0% --- ## General course Outline 1) Introduction to R and Rstudio 2) Foundational concepts for statistical inference 3) Summary Statistics 4) Basic linear models 5) Null hypothesis significance testing 6) Comparing means (t-tests, ANOVA) 7) Regressions --- ## Looking Forward **Homework:** * Download R and RStudio (in that order) on personal computer (if using) * Next class: Rstudio Projects * If time allows: Brief tour of Rstudio + Follow along on ENVS laptops or personal if software already downloaded