### 20.2.5 Exponential regression

You might expect a set of points to lie on an exponential curve y=b ax. The exponential_regression command finds the values of a and b which give you the best fit exponential.

- exponential_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).
- exponential_regression(xcoords,ycoords) returns a sequence a,b of the numbers in the best fit exponential y=bax.

#### Example

evalf(exponential_regression([[1,1],[2,4],[3,9],[4,16]]))

or:

evalf(exponential_regression([1,2,3,4],[1,4,9,16]))

(where the evalf is used to get a numeric approximation to an exact expression, see Section 7.3.1).

2.49146187923,0.5 |  |  |  |  |  |  |  |  |  |

so the best fit exponential curve will be y=0.5·(2.49146187923)x.

The exponential_regression_plot command draws the best fit exponential.

- exponential_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).
- exponential_regression_plot(xcoords,ycoords) draws the best fit exponential, and puts the equation and R2 value above the graph.

#### Example

exponential_regression_plot([1,2,3,4],[1,4,9,16])