[regression] objective function section done

regression/code/meanSquaredError.m

@@ -1,12 +0,0 @@
-function mse = meanSquaredError(x, y, parameter)
-% Mean squared error between a straight line and data pairs.
-%
-% Arguments: x, vector of the input values
-%            y, vector of the corresponding measured output values
-%            parameter, vector containing slope and intercept 
-%                       as the 1st and 2nd element, respectively. 
-%
-% Returns:   mse, the mean-squared-error.
-
-  mse = mean((y - x * parameter(1) - parameter(2)).^2);
-end

regression/code/meanSquaredErrorCubic.m

@@ -0,0 +1,11 @@
+function mse = meanSquaredError(x, y, c)
+% Mean squared error between data pairs and a cubic relation.
+%
+% Arguments: x, vector of the input values
+%            y, vector of the corresponding measured output values
+%            c, the factor for the cubic relation. 
+%
+% Returns:   mse, the mean-squared-error.
+
+  mse = mean((y - c*x.^3).^2);
+end

regression/code/meansquarederrorline.m

@@ -10,6 +10,6 @@ yest = c * x.^3;
 y = yest + noise*randn(n, 1);
 
 % compute mean squared error:
-mse = mean((y - y_est).^2);
+mse = mean((y - yest).^2);
 
-fprintf('the mean squared error is %g kg^2\n', mse))
+fprintf('the mean squared error is %.0f kg^2\n', mse)