BIO3011 Lecture Notes - Lecture 5: Random Effects Model, Regression Analysis, Dependent And Independent Variables

64 views2 pages
5. Basic Statistics II
The most important thing to identify at the start of any analysis is what type of response variable
you have
Relationship between test statistics, DF and P value
Variance test: as DF increases, P value decreases
Chi-squared: as DF increase, P value increase
Mixed effects models:
Only need to understand how they are used and why they are important
Random effects are a way to fix pseudoreplication without having to average everything (group
factor)
Can be thought of as a modified form of ANOVA or regression analysis
Used to split predictors into fixed effects and random effects
Fixed predictor effects influence the mean usually the things we set up an experiment to study
Random predictor effects influence the variance usually the things we want to control for in a
study
A group effect is random if the levels within the group are samples from a larger population
eg. if we took parasite samples from ten individual echidnas at ten sites (100 samples), the SITE
is a random effect.
Eg. 100 ants collected from 5 ant nests
-> 5 independent samples
-> use nest as a random effect and put extra column as nest
find more resources at oneclass.com
find more resources at oneclass.com
Unlock document

This preview shows half of the first page of the document.
Unlock all 2 pages and 3 million more documents.

Already have an account? Log in

Get access

Grade+20% off
$8 USD/m$10 USD/m
Billed $96 USD annually
Grade+
Homework Help
Study Guides
Textbook Solutions
Class Notes
Textbook Notes
Booster Class
40 Verified Answers
Class+
$8 USD/m
Billed $96 USD annually
Class+
Homework Help
Study Guides
Textbook Solutions
Class Notes
Textbook Notes
Booster Class
30 Verified Answers

Related textbook solutions

Related Documents

Related Questions