1. Methods in Experimental Ecology II

2. Why? Pseudo-replication Unnecessary complicated analysis
Weak inference
Confounding variables

3. Methods in Experimental Ecology I
Experimental Design and Pseud-replication
Probability Distributions
Data with normal errors
Summary statistics
Confidence Intervals
Regression
Model Selection approaches
Statistics Dept.
Methods in Experimental Ecology II
Bayesian and Frequentist approaches
Complex data
General Linear Models
Mix-models
Non-linear Models
Data modeling
Mathematics Dept.
Computer Science Dept.

4. Approach Understanding the attributes of the data using generated data
Implementation of analysis
Identification of model limitations
Understanding differences between approaches
Application to case studies

5. Bayesian Analysis of averages

6. Analysis of count data
Gregory Territo (Parkinson’s Lab)

7. Wildebeest carcasses from the Serengeti (Sinclair and Arcese 1995)
Analysis of categorical data
Females
Marrow
Death OG
SWF TG
Total
PRED 32 26 8 66
NPRED 6 16 48 58 24
114
Wildebeest carcasses from the Serengeti (Sinclair and Arcese 1995)
Males
Marrow
Death OG
SWF TG
Total
PRED 43 14 10 67
NPRED 12 7 26 45 55 21 36
112

9. R However, I will teach using R because
Students can use any platform they feel comfortable
However, I will teach using R because
It is free
It has reasonable documentation in the web
It is flexible
It has many applications for biological purposes