Regression is about determining the unknown parameters of a growth model or a given function for a series of measurement data in such a way that the final model adapts to the data as best as possible.
Frequently considered models are:
- Linear Growth
- With linear growth, the rate of change, i.e. the derivation of the growth function, is constant.
The corresponding diagram is a straight line.
- Exponential Growth
- With exponential growth, the rate of change is proportional to the population:
- Limited Growth
- Whith limited growth, the rate of change is proportional to the saturation deficit, that is the difference between the saturation limit S and the population:
- Logistical Growth
- With logistical growth, it is assumed that the population grows essentially exponentially at the beginning, but that growth is slowed down more and more as the saturation limit is approached.
It is therefore assumed that the rate of change is proportional to both the population and the saturation deficit. This results in the differential equation:
Which has the solution:
For a given saturation limit S , the program determines the initial value f(0) and the proportionality factor k for adapting the function f (t) to the given value pairs.