Changes

*i.e.,The Least Squares method relies on taking partial derivatives with respect to the slope  

*i.e.,The Least Squares method relies on taking partial derivatives with respect to the slope  

and intercept which provides a solvable pair of equations called normal equations.

and intercept which provides a solvable pair of equations called normal equations.

*Suppose there are <math> n </math> data points {y_{i}, x_{i}}, where i = 1, 2, …, n.

*Suppose there are <math> n </math> data points<math> {y_{i}, x_{i}}</math>, where i = 1, 2, …, n.

*To find the equation of the regression line:<math> a=y(bar)-b.x(bar)</math>.

*To find the equation of the regression line:<math> a=y(bar)-b.x(bar)</math>.

*This equation will give a "best" fit for the data points.  

*This equation will give a "best" fit for the data points.