Reference

Reference

Additional reading:

Data analysis recipes: Fitting a model to data, Hogg et al.

An Introduction to Statistical Learning with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani contains additional learning on statistical and machine learning, including topics such as cross-validation. The book is also accompanied by code written in R.

The Elements of Statistical Learning, by Trevor Hastie, Robert Tisbshirani and Jerome Friedman is a more heavily mathematical treatment of similar topics:

Leo Breiman’s The two cultures of statistical modeling makes some observations about different approaches to models, their predictive, and explanatory power.

Glossary

Model

A function that describes the relationship between dependent and independent variables in the data.

Model parameters

Variables of the model that are neiter dependent variables or independent variables in the data, but adjust the relationship between them, as described by the model. These sometimes correspond to underlying constructs of interest in the data. In some cases, additional calculations on the parameters can be used to make inferences about these constructs.