 
 
 
20.2.6  Logarithmic regression
You might expect a set of points to lie on a logarithmic curve
y=m ln(x)+b.
The logarithmic_regression
command finds the logarithmic curve of best fit.
- 
logarithmic_regression takes two arguments:
- 
xcoords, a list of x-coordinates.
- ycoords, a list of y-coordinates.
 You can combine two arguments into a matrix with two columns (each
list becomes a column of the matrix).
- logarithmic_regression(xcoords,ycoords)
returns a sequence m,b of the numbers in the best fit logarithmic
curve y=m ln(x)+b.
Example
| evalf(logarithmic_regression([[1,1],[2,4],[3,9],[4,16]])) | 
or:
| evalf(logarithmic_regression([1,2,3,4],[1,4,9,16])) | 
(where the evalf is used to get a numeric approximation to an
exact expression):
|  | | 10.1506450002,−0.564824055818 |  |  |  |  |  |  |  |  |  |  | 
 | 
so the best fit logarithmic curve will be
y=10.1506450002 ln(x) −0.564824055818.
The logarithmic_regression_plot
command draws the best fit logarithmic curve.
- 
logarithmic_regression_plot takes two arguments:
- 
xcoords, a list of x-coordinates.
- ycoords, a list of y-coordinates.
 You can combine two arguments into a matrix with two columns (each
list becomes a column of the matrix).
- logarithmic_regression_plot(xcoords,ycoords)
draws the best fit logarithmic curve, and puts the equation and R2
value above the graph.
Example
| logarithmic_regression_plot([1,2,3,4],[1,4,9,16]) | 
 
 
