The power regression option finds the equation of an equation of
the form y = axb that best fits a set of data.
enter the data .
to view the statistics calculation options.
The values of both x and y must be greater than zero. (This is because the method for
determining the values of a and b in the regression equation is a least-squares fit on
the values for ln x and ln y.)
Enter the columns to be used for the x and y entries, c1 and c2 in this case.
The regression equation can be automatically stored in the equation editor by selecting
the Stor RegEQ.
Therefore, the best-fit power equation for this data is approximately
y = 0.84x1.59.
Note: The TI-92 calculates the correlation coefficient, r and the correlation of determination,