Power regression
Purpose
Description
Macro Synopsis
Modules
Related Functions
References
Purpose
Power regression.
Description
"Power regression", also known as log-log regression, takes the input signal and fits a function
to it where t is the variable along the x-axis.
The function is based on the function Linear regression, with both axes scaled logarithmically.
Return parameters of Power regression are:
- the amplitude a and
- the exponent b of the fitting function.
Besides a and b, the corresponding uncertainties sigma a and sigma b and the correlation coefficient r, will appear in the message window. This coefficent is given by
where the overline denotes mean values.
Values of r near 0 indicate poor fitting, whereas good fitting is indicated by values of r near +1 or -1, depending on whether the exponent b of the fitting function is positive or negative.
Warning: Make sure that the input signal does not cross any of the axes x=0 or y=0. Otherwise an error will be produced.
Macro Synopsis
y = PowRegress(x,&a,&b);
signal x,y;
float a,b;
Modules
Statistics
Related Functions
Linear regression, Exponential regression,
Multilinear regression, Remove DC,
Remove trend, Box-Cox transform, Detrended fluctuation analysis (DFA),
Long term correlation analysis (LTCA),
Principal component analysis (PCA),
R/S statistics.
References
Graybill [12]