Projected figures vary depending on underlying statistical assumptions and the variables used in projection calculations, especially the fertility variable.

Regression analysis Many of the standard methods for these tasks rely on certain statistical assumptions (made in the derivation of the methodology) actually holding in practice.

They rely on statistical assumptions which are fraught with error and they can be manipulated for political purposes.

This leads to the opportunity to optimise the leak decision if some statistical assumptions hold.

However, fixed-value thresholds (like 0.5, or 0.143) were argued to be based on incorrect statistical assumptions.

However, determining the resolution threshold remains a controversial issue: fixed-value thresholds were argued to be based on incorrect statistical assumptions.

Algorithmic learning theory is different from statistical learning theory in that it does not make use of statistical assumptions and analysis.

The most commonly applied statistical assumptions are:

There may be spatial trends and spatial autocorrelation in the variables that violates statistical assumptions of regression.

Alternatively, it allows modellers to choose freely of technical constraints the shapes of their statistical assumptions.