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==Description==
==Description==
*This function gives the slope of the linear regression line through a set of given points.
*This function gives the slope of the linear regression line through a set of given points.
*The slope of a regression line (b) represents the rate of change in y as x changes.
*The slope of a regression line (b) represents the rate of change in <math> y </math> as ,math> x </math> changes.
*To find a slope we can use the least squares method.
*To find a slope we can use the least squares method.
*Slope is found by calculating b as the covariance of x and y, divided by the sum of squares (variance) of x.
*Slope is found by calculating b as the covariance of x and y, divided by the sum of squares (variance) of x.
*In SLOPE(y,x), y is the array of the numeric dependent values and x is the array of the independent values.
*In <math>SLOPE(y,x), y </math> is the array of the numeric dependent values and <math> x </math> is the array of the independent values.
*The arguments can be be either numbers or names, array,constants or references that contain numbers.
*The arguments can be be either numbers or names, array,constants or references that contain numbers.
*Suppose the array contains text,logical values or empty cells, like that values are not considered.
*Suppose the array contains text,logical values or empty cells, like that values are not considered.
*The equation for the slope of the regression line is :<math>b = \frac {\sum (x-\bar{x})(y-\bar{y})} {\sum(x-\bar{x})^2</math>. where <math>\bar{x}</math> and <math>\bar{y}</math> are the sample mean x and y.
*The equation for the slope of the regression line is :<math>b = \frac {\sum (x-\bar{x})(y-\bar{y})} {\sum(x-\bar{x})^2}</math>. where <math>\bar{x}</math> and <math>\bar{y}</math> are the sample mean x and y.
*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 nonnumeric.
1. Any one of the argument is nonnumeric.