Statistical Modelling III
Content for Wednesday, November 8, 2023
In our last class on multivariate analysis, we’ll take on one of the more underappreciated elements of modeling: understanding if your model is good enough for prediction or inference. We’ll spend a bit of time differentiating the uses of models as a means of understanding what it means to be a “good” model.
Resources
A practical guide to selecting models for exploration, inference, and prediction in ecology by (Tredennick et al. 2021) highlights the importance of understanding model performance before making inference on predictor effects.
Model selection using information criteria, but is the “best” model any good? by (Mac Nally et al. 2018) highlights the importance of understanding model performance before making inference on predictor effects.
Standards for distribution models in biodiversity assessments by (Araújo et al. 2019) highlights the importance of understanding model performance before making inference on predictor effects.
Objectives
By the end of today you should be able to:
Articulate three different reasons for modeling and how they link to assessments of fit
Describe and implement several test statistics for assessing model fit
Describe and implement several assessments of classification
Describe and implement resampling techniques to estimate predictive performance