To assist with the comprehension of our proposed framework, we apply it to the logistic regression model for illustrative purposes.
For this purpose, regression models were developed for predicting the critical wind speeds needed to uproot Scots pine, Norway spruce and birch trees at the downwind stand edges in Finnish conditions under unfrozen soil conditions, based on the characteristics of both downwind and upwind stand, and additional snow load on tree crowns.
These features were then used to construct a linear regression model for predictive purposes.
A graph predicting 'any diagnosis' was generated from the regression model for descriptive purposes.
Additive generalised models and mixed Poisson regression models were used for the purpose, taking year as the random-effect variable and adjusting for age, sex, prevalence of vascular risk factors and the number of hospital beds in intensive and coronary care units.
All study variables were centralized before being entered into the hierarchical linear regression model for the purpose of avoiding multicollinearity [ 40].
For this purpose the regression model is usually adjusted for age, pT stage, grading, hormone (estrogen and progesterone) receptor as well as ERBB2 status [ 38].
The purpose of this Monte Carlo simulation experiment was to investigate the properties of the linear regression model, the beta regression model, the variable-dispersion beta regression model and the fractional logit regression model at recovering average proportion/percentage/rate differences from a two sample design.
It was chosen to present the root mean squared error, the mean absolute error, and the R-squared for each of the regression models for descriptive purposes.
For this purpose we use a logistic regression model, and study how contextual features impact our ability to predict an individual's next place.
