“All models are wrong but some are useful.” George Box
“Everything should be as simple as it can be, but not simpler.” Albert Einstein
A statistical model is means to capture the structure of the data as simply as possible. Put another way, our goal is to find the model that most efficiently and accurately summarizes how the data were generated.
Introduction to linear mixed models
R for Statistical Learning by David Dalpiaz. Chapter 4 Modeling Basics in R
Statistical Thinking for the 21st Century by Russell A. Poldrack. Chapter 5 Fitting models to data.
Data Science with R by Garrett Grolemund. Chapter 3 Model Fitting.
Heteroscedasticity means unequal scatter. In regression analysis, we talk about heteroscedasticity in the context of the residuals or error term. Specifically, heteroscedasticity is a systematic change in the spread of the residuals over the range of measured values. Heteroscedasticity is a problem because ordinary least squares (OLS) regression assumes that all residuals are drawn from a population that has a constant variance (homoscedasticity).
Heteroscedasticity in Regression Analysis By Jim Frost