[regression] started to simplify chapter to a 1d problem

regression/code/meanSquaredErrorLine.m

@@ -1 +0,0 @@
-mse = mean((y - y_est).^2);

regression/code/meansquarederrorline.m

@@ -0,0 +1,15 @@
+n = 40;
+xmin = 2.2;
+xmax = 3.9;
+c = 6.0;
+noise = 50.0;
+
+% generate data:
+x = rand(n, 1) * (xmax-xmin) + xmin;
+yest = c * x.^3;
+y = yest + noise*randn(n, 1);
+
+% compute mean squared error:
+mse = mean((y - y_est).^2);
+
+fprintf('the mean squared error is %g kg^2\n', mse))