In a Machine Learning tutorial, I came across the following equation for a the cost function of linear regression.
$$T(\theta)=(\frac{1}{2m})(\sum_1^m{(\theta_0+\theta_1x_1^i+\theta_2x_2^i+...+\theta_nx_n^i - y^i)^2}) $$
m is the number of known results and n is the number of features. Take n=2 and does not consider theta_0, and draw the T against theta_1 and theta_2, we would get a set of contours.
If the range of x1 is 0 to 1000 and the range of x2 is 0 to 1, it says that the contours skew towards the theta_2 axis. Could someone please show how one can get to that conclusion mathematically ?