To predict location, we use the k nearest neighbors to a set of signal strengths. We average the known (x, y) values for these neighbors. However, a better predictor might be a weighted average, where the weights are inversely proportional to the "distance" (in signal strength) from the test observation. This allows us to include the k points that are close, but to differentiate between them by how close they actually are. The weights might be
for the i-th closest neighboring observation where di is the distance from our new test point to this reference point (in signal strength space). Implement this alternative prediction method. Does this improve the predictions? Use calc Error () to compare this approach to the simple average.