Methods in Experimental Ecology I (PCB 6466): Fall 2021

Course Information
  • Time: Wednesdays & Fridays  (2:00-3:20)
  • Room: Bio 305
  • Instructor: Dave Jenkins, office = BIO 111B, email [david.jenkins AT ucf.edu] for help / meetings
  • TA: Fede Borghesi office = BIO 111, email [flopezborghesi AT knights.ucf.edu] for help / meetings

Textbook: Hector. 2015. The New Statistics with R. Available FREE thru UCF Library (with your login)

Resources

Description

Learn how to design, analyze and interpret experiments and quantitative observations. Introduction to modern statistical software and basic statistical methods needed to collect, organize and interpret data critically. For beginning graduate and senior undergraduate researchers, this course bridges between undergraduate stats and Methods II.

TENTATIVE SCHEDULE (subject to hurricanes, changes in covid policies, etc.)
Week Videos etc. (before class) Reading (before class) In-Class Topics Assignments
1:

Aug 25, 27

Passcode: 4%T3$?%=

Installing R & RStudio

RStudio Orientation

swirlstats.com

data carpentry

Plot: generic X-Y Plotting

Popularity of stats software

Much ado about p

But stats are > p

Lederer et al. (2018)

Anscombe’s quartet

Hector Ch. 1 & Appendix

Course overview & goals

The new statistics

Intro to R & RStudio

  1. Install R & RStudio.
  2. Make a plot in R. Any plot.
  3. Email it to Fedé
2:

Sep 1, 3

Passcode: J3sE.+LT

 

 

Data basics

Handling data

dplyr lesson 1

dplyr lesson 2

Hypotheses 1 , 2

Chamberlin (1890)

McGill et al. (2006) excerpt

Ecology & strong inference

Chaves (2010)

Buchanan (2019)

Readings discussion

Handling Data (v2) in R

SSA babynames

Homework #1

(Due 8 Sep)

3:

Sep 8, 10

Experimental Designs 1

Experimental Designs 2

Helicopter experiment

 

So many choices

Playing with copter data

helicopter data.csv

helicopter data.xls

Homework #2

(Due 15 Sep)

4:

Sep 15, 17

How To Graph in RStudio: The Basics

ggplot 2, lecture #1

ggplot 2, lecture #2

Graphing in R ggplot2 #1

Graphing in R ggplot2 #2

Tufte in R

Getting Started with Charts in R

Plot: generic X-Y Plotting

R cheat sheets (!) including ggplot

cowplot, lattice

Graphing I (now as plain .txt)

Graphing II (as plain .txt)

Copter data

Homework #3

Silwood weather data

(Due 22 Sep)

5:

Sep 22, 24

Probabilities

Distributions

Normality tests I

Normality tests II

Homogeneity tests

Do not log-transform count data

Normality & Variance

Transforming Data

Homework #4

species pH data

(Due 29 Sep)

6:

Sep 29, Oct 1

Z- and t-tests

ANOVAs I

Hector (2015) Ch. 2 & 3

t tests

NC births data

Copter ANOVAs 1

Homework #5

zinc data

barley data

possum data

(Due 6 Oct)

7:

Oct 6, 8

ANOVAs II

Model selection (w/ AIC)

Hector (2015) Ch. 6

Anderson et al. (2000)

Anderson & Burnham (2002)

Aho et al. (2014)

Readings discussion

Copter ANOVAs 2

Using AIC

Homework #6

wheat data

censusrb data

cancer data

(Due 13 Oct)

8:

Oct 13, 15

Representing statistical variation

Hector (2015) Ch. 5

Min. N for regressions

CIs

CIs.R (a txt file)

Power analysis

Homework #7

chickwts ipomopsis

(Due 20 Oct)

9:

Oct 20

 

OLS Regressions

SMA Regressions

FRI: F’Ball game

Hector (2015) Ch. 6

Jenkins & Quintana-Ascencio (2020)

CIs, not posthoc power!

Warton et al. (2012)

Linear regressions

tancat   FLHg

SMA regression

PB

Homework #8

MLBbatting2010 fishery

(Due 27 Oct)

10:

Oct 27, 29

ANCOVAs

Multiple Regressions

Hector (2015) Ch. 7

ANCOVAs

Multiple Regressions

Homework #9

timber2

(Due 3 Nov)

11:

Nov 3, 5

Generalized Linear Models I

Generalized Linear Models II

Hector (2015) Ch. 8, 9 GLMs 1

GLMs 2 & CART

ozone data

Homework #10

(Due 10 Nov)

12:

Nov 10, 12

 

Intro to Mixed Effects Models

Hector (2015) Ch. 10

Mixed Effects ANOVAs

plantdata0615

Homework #11

carrots

(Due 17 Nov)

13:

Nov 17, 19

 

Logistic Regressions

 

Mixed Effects Regressions

Logistic Regressions

islandbird

Homework #12

flowering

parasites

(Due 24 Nov)

14:

Nov 24

Intro to Multivariate Analyses

FRI: T’GIVING

CRAN Multivariate Task View

NMDS in R

Ordination Overview

ordinations.R.txt

diplo2017

Ohyama et al. (2018)

no homework
15:

Dec 1, 3

– – – –

– – – – Study design & analyses, revisited

Review & in-class practice

sulphur.dioxide.txt

Cumulative Final Exam to you 3rd

no homework
16

Finals Week

Finals Week  Finals Week

Final DUE FRI DEC 10th