Linear Regression Slope Calculator Formula

Understand the math behind the linear regression slope calculator. Each variable explained with a worked example.

Formulas Used

Slope (b1)

slope = slope_num / slope_den

Mean of x

x_mean = sum_x / n

Mean of y

y_mean = sum_y / n

Variables

VariableDescriptionDefault
nNumber of Pairs5
sum_xySum of (x*y)2350
sum_xSum of x75
sum_ySum of y150
sum_x2Sum of x-squared1175
slope_numDerived value= n * sum_xy - sum_x * sum_ycalculated
slope_denDerived value= n * sum_x2 - pow(sum_x, 2)calculated

How It Works

How to Calculate the Regression Slope

Formula

b1 = [n*Sum(xy) - Sum(x)*Sum(y)] / [n*Sum(x^2) - (Sum(x))^2]

The slope of the least-squares regression line represents the predicted change in Y for a one-unit increase in X. A positive slope indicates a positive relationship; negative means Y decreases as X increases.

Worked Example

n = 5, Sum(xy) = 2350, Sum(x) = 75, Sum(y) = 150, Sum(x^2) = 1175.

n = 5sum_xy = 2350sum_x = 75sum_y = 150sum_x2 = 1175
  1. 01Numerator = 5*2350 - 75*150 = 11750 - 11250 = 500
  2. 02Denominator = 5*1175 - 75^2 = 5875 - 5625 = 250
  3. 03Slope b1 = 500 / 250 = 2.0
  4. 04For each unit increase in x, y increases by 2 on average

Frequently Asked Questions

What if the slope is zero?

A slope of zero means there is no linear relationship between X and Y. The best prediction for Y is simply the mean of Y, regardless of X.

Is the regression slope the same as correlation?

No. The slope depends on the scales of X and Y (it has units of Y/X). The correlation r is dimensionless and standardized between -1 and 1. They share the same sign but different magnitudes.

What does "least squares" mean?

The least-squares method finds the line that minimizes the sum of squared vertical distances (residuals) between observed Y values and predicted Y values. This gives the best linear unbiased estimate under certain assumptions.

Learn More

Guide

Regression Analysis Guide

Comprehensive guide to regression analysis. Learn how linear regression works, how to interpret slope and intercept, R-squared, residuals, and when to use regression.