Statistical

SLOPE Formula

SLOPE returns the slope of the linear regression line fitted to two data sets (known y-values and known x-values). The slope tells you how much y changes for each one-unit increase in x. Use it for trend analysis, forecasting, and understanding the rate of change in relationships like cost per unit, revenue growth per month, or temperature change over time.

Syntax

SLOPE(known_y, known_x)
ParameterDescription
known_y Parameter of the SLOPE function.
known_x Parameter of the SLOPE function.
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Examples

Revenue growth rate

Formula
=SLOPE(B2:B13, A2:A13)
If B contains monthly revenue and A contains month numbers (1-12), returns the average monthly revenue increase. A slope of 5000 means revenue grows ~$5,000/month.

Cost per unit

Formula
=SLOPE(C2:C50, D2:D50)
If C is total cost and D is units produced, the slope represents the marginal cost per additional unit.

Negative trend

Formula
=SLOPE(E2:E365, ROW(E2:E365))
A negative slope indicates a declining trend — values are decreasing over time.

Common Errors

#N/A

The arrays have different numbers of data points. Known_y and known_x must be the same length.

#DIV/0!

All x-values are identical (zero variance in x). A vertical line has undefined slope.

Tips

Argument order matters

SLOPE(y, x) — the dependent variable (y) comes first, then the independent variable (x). Getting them reversed gives the reciprocal of the correct slope.

Use with INTERCEPT for the full equation

The regression line is y = SLOPE*x + INTERCEPT. With both values, you can predict y for any x value.

Check RSQ first

A slope is meaningless if the relationship is weak. Check RSQ(y,x) first — if it is low (below 0.5), the slope may not be a reliable predictor.

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