Changes

==Description==

==Description==

*This function gives the standard error of the regression, which also is known as the standard error of the estimate.  

*This function gives the standard error of the regression, which also is known as the standard error of the estimate.  

*It is calculates the  standard error for the straight line of best fit through a supplied set of <math> x </math> and <math> y </math> values.  

*It is calculates the  standard error for the straight line of best fit through a supplied set of <math> KnownXs</math> and <math> KnownYs</math> values.  

*The standard error for this line provides a measure of the error in the prediction of <math> y </math> for an individual <math> x </math>.  

*The standard error for this line provides a measure of the error in the prediction of <math> KnownYs </math> for an individual <math> KnownXs </math>.  

*The equation for the standard error of the predicted <math> y </math> is:  

*The equation for the standard error of the predicted <math> y </math> is:  

<math>\sqrt{\frac{1}{(n-2)}\left [ \sum(y-\bar{y})^2-\frac{[\sum(x-\bar{x})(y-\bar{y})]^2}{\sum(x-\bar{x})^2} \right ]}</math>

<math>\sqrt{\frac{1}{(n-2)}\left [ \sum(y-\bar{y})^2-\frac{[\sum(x-\bar{x})(y-\bar{y})]^2}{\sum(x-\bar{x})^2} \right ]}</math>

*This function will return the result as error when

*This function will return the result as error when

   1. Any one of the argument is non-numeric.  

   1. Any one of the argument is non-numeric.  

   2. <math>KnownYs</math> and <math>KnownXs</math> are empty or that have less than three data points.

   2. KnownYs and KnownXs are empty or that have less than three data points.

   3. <math>KnownYs</math> and <math>KnownXs </math> have a different number of data points.

   3. KnownYs and KnownXs have a different number of data points.

==Examples==

==Examples==