SLOPE(array_y, array_x)

The SLOPE function calculates the slope of the linear regression line through the data points described by array_x (the independent or controlled variable) and array_y (the dependent variable).

The slope is the vertical distance between any two data points divided by the horizontal distance between the same two data points. It is a measure of the rate of change of the regression line, and is given by the formula:

image\ebx_-133911938.gif

where b is the slope of the regression line.

The x- and y-arrays or ranges must have the same number of data points.

For example, suppose the following y-values were observed for the supplied x-values:

Y

4.7

6.0

11.2

10.6

8.2

7.3

15.8

11.7

X

1

2

3

4

5

6

7

8

A linear regression through these points would look like this:

image\ebx_345652550.gif

You calculate the slope of the line using:

SLOPE({4.7, 6, 11.2, 10.6, 8.2, 7.3, 15.8, 11.7}, {1, 2, 3, 4, 5, 6, 7, 8})

which returns 0.9988.

See also:

FORECAST(x, array_y, array_x)

INTERCEPT(array_y, array_x)

STEYX(array_y, array_x)

Other statistical functions