LECTURE 0: course overview

class: center, middle, inverse, title-slide

.title[
# LECTURE 0: course overview
]
.subtitle[
## FANR 6750 (Experimental design)
]
.author[
### Fall 2024
]

---

# logistics

**Lecture**: Monday, Wednesday, Friday 11:30-12:20, 1-304  
**Lab**: Monday **or** Tuesday **or** Wednesday 
**Credits**: 3

---
# instructors

.pull-left[
Dr. Clark Rushing  
[clark.rushing@uga.edu](clark.rushing@uga.edu)   
**Office**: 3-409  
**Office hours**: M 12:30-2:00; Tu 9:00-10:00
]

.pull-right[

**TAs**

Kaili Gregory (M)  
[k.gregory@uga.edu](k.gregory@uga.edu)  
**Office**: 3-422  
**Office hours**: Tu 3:00-5:00

Michael Baker (Tu)  
[michael.baker2@uga.edu](michael.baker2@uga.edu)  
**Office**: 1-102A  
**Office hours**: F 3:00-5:00

Alan Bond (W)  
[alan.bond1@uga.edu](alan.bond1@uga.edu)  
**Office**: 1-102  
**Office hours**: M 2:00-4:00  
]

---
# course schedule and materials

Lectures and labs: [rushinglab.github.io/FANR6750](https://rushinglab.github.io/FANR6750)1

.footnote[[1] Bookmark this page!]

.pull-left[
**Primary texts** (not required):

Fieberg, J. (2022). Statistics for Ecologists: A Frequentist and Bayesian Treatment of Modern Regression Models. [An open-source online textbook.](https://fw8051statistics4ecologists.netlify.app/)

Quinn, G.P. & Keough, M.J. 2002. Experimental Design and Data Analysis for Biologists. Cambridge University Press
]

.pull-right[
<img src="fig/experimental-design-and-data-analysis-for-biologists-1.jpg" width="225" height="300" />
]

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# labs

**Meet weekly**

- You should have registered for one and only one lab section

- Always attend your assigned lab section unless both TA's have provided prior approval to attend the other section

**Taught in R**

- No prior experience required

- But those without prior experience may need to spend time learning outside of class

- You can use your own laptop but make sure you have R and RStudio installed prior the first lab1

.footnote[[1] See [here](https://rushinglab.github.io/FANR6750/articles/syllabus.html#course-resources-1) for instructions]

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# lab assignments

- 8 throughout semester

- Meant to help with:
    + Understanding lecture/lab concepts
    + Implementing models in R
    + Interpreting and presenting results
    
--

- Worth 12.5 points each
    + 6.5 points for turning in **complete** assignment **on time**
    + 6 points for correctness

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# grading

#### 250 points total

- 3 lecture exams, 50 points (20%) each

+ Take-home, open-note format
 + Not (explicitly) cummulative1
 + See schedule for approximate dates (subject to change)

- 8 lab assignments, 12.5 points (5%) each

.footnote[[1] Material is somewhat cumulative by nature & some important concepts will be repeated]

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# a note on AI

#### AI tools (e.g., ChatGPT) have advanced very rapidly in a very short time

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- AI can increasingly help answer questions about statistical concepts, coding, etc.

#### AI = resource (similar to Google, Cross Validated, etc.)

- AI can help you perform statistical analyses but **it cannot replace your own statistical knowledge**

#### My goal is not to fight AI, but instead to incorporate it into exams and assignments

- Learn to properly and ethically use AI, including when to be skeptical

- This will be a learning experience for all of us

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# course objectives

**To understand:**

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1) the logical structure of experiments, especially the design of manipulative experiments;

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2) the analysis of such experiments, focusing on linear models;

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3) the use of models in ecological studies (experimental and observational);

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# basic structure

1) Foundational concepts for statistical inference

2) Linear model basics

3) Null hypothesis significance testing

4) Linear model variations for experiments (t-tests, ANOVA, ANCOVA)

5) Generalized linear models and model selection

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# looking ahead

### Next time: Basic Concepts in Statistics

### Reading: Quinn chp. 1