[- regression methods]
PROBLEM [data analysis] - what is the best way to maximise any type of information? to understand any behaviour?
- the ideal result would be a doubtless answer, that is, a mathematical answer -
regression methods - determine the influence from the independent variables behaviour [or predictors] over the dependent one [or measurement]
advantage
- acceptable accuracy and "relatively controlable uncertainty" (no arbitrary user intervention)
disadvantage
- maximum number of independent variables = practically limited
advantage
- acceptable accuracy and "relatively controlable uncertainty" (no arbitrary user intervention)
disadvantage
- maximum number of independent variables = practically limited
SOLUTION?
1. Virtually, any information can be adapted to the aforementioned duality [independent/dependent variables]; however, this is a problem common to all the forecasting methods
1. Virtually, any information can be adapted to the aforementioned duality [independent/dependent variables]; however, this is a problem common to all the forecasting methods
NOTE: any data suitable to be predicted, that is, not random
2. ideas to bear in mind* NEVER extrapolate
* predictive character only under certain conditions [i.e., minimum repetitions & goodness of the fit]
* weightages, user defined parameters, etc. only under extreme circumstances => "no trend" has to be as valid as any numerical result
* predictive character only under certain conditions [i.e., minimum repetitions & goodness of the fit]
* weightages, user defined parameters, etc. only under extreme circumstances => "no trend" has to be as valid as any numerical result
but... no behaviour [at least, no one worthly to be predicted] can be described by attending at a low number of variables
NOTE: this limitations regarding the number of independent variables will be treated in future posts
NOTE: this limitations regarding the number of independent variables will be treated in future posts
