LECTURE 0: Course overview

class: center, middle, inverse, title-slide

.title[
# LECTURE 0: Course overview
]
.subtitle[
## FANR 6750 (Experimental design)
]
.author[
### Fall 2022
]

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# logistics

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

---
# instructors

#### Dr. Clark Rushing  
[clark.rushing@uga.edu](clark.rushing@uga.edu)   
**Office**: 3-409  
**Office hours**: Monday and Wednesday 1:00-2:30 (or by appointment)

#### Michael Baker  
[michael.baker2@uga.edu](michael.baker2@uga.edu)  
**Office**: 1-102A  
**Office hours**: W 9:30-11:00 or by appointment  
  
#### Nancy Raginski  
[nancy.raginski@uga.edu](nancy.raginski@uga.edu)  
**Office**: 3-402  
**Office hours**: Th 10-11:30 or by appointment

---
# course schedule and materials

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

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

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="262" height="350" />
]
.footnote[[1] Bookmark this page!]

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# labs

**Meet weekly**

- You should have registered for either Monday **or** Tuesday

- 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

- 5 throughout semester (approximately bi-weekly)

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

- Worth 10 points each
    + 6 points for turning in **complete** assignment **on time**
    + 2 points for correcting assignment (using a key)
    + 2 points for completed self-assessment form

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# grading

#### 200 points total

- 3 lecture exams, 50 points (25%) each

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

- 5 lab assignments, 10 points (5%) each

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

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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