Curve fitting as a linear least squares approximation problem? So I have a problem from a textbook that will count for a bonus homework assignment, but I am having some trouble knowing where to start.

Some more difficult curve fitting problems can be converted to linear
  least squares approximation problems. One example is the fitting
  function $y = a\,x^u$ where both $a$ and $u$ are to be determined. Suppose
  $(x_j, y_j), x_j > 0, y_j > 0, 1 \le j \le n$, are given points. We apply
  the natural log to rewrite the fitting function as $\ln(y)  = \ln(a) +
 u \ln(x)$. Demonstrate how to determine $a$ and $u$.

Could anyone maybe help point me in the right direction or give an explanation?

Applying the logarithm to both sides
$$
y = a x^u \Rightarrow \\
\ln y = Y = \ln(a) + u \ln(x) = Y_0 + u X
$$
turns the exponential curve into a linear curve $Y(X)$, so you can apply the regression techniques for linear functions to it, yielding the $Y_0$ and $u$ for the best fit linear curve.
Obviously $\ln(a) = Y_0 \Rightarrow a = \exp(Y_0)$.
