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?

This is basic regression. transform your data by taking logs. Run your regression. You'll get $\hat\beta_0$ and $\hat\beta_1$ To get $a$: $a=e^{\hat \beta_0}$ and $u=\hat\beta_1$. 
Transform problems are common in stats/ probability fyi. Always look for them, they'll make your life easier.
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