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The least-squares slope is calculated as the sum of the products of centered x and y values, divided by the sum of squared centered x values. Mathematically, this is $\Sigma[(x_i - \bar{x})(y_i - \bar{y})] / \Sigma(x_i - \bar{x})^2$.

Conditions: The denominator $\Sigma(x_i - \bar{x})^2$ is positive.; The model is simple linear regression with an intercept.

The least-squares slope is calculated as the sum of the products of centered x and y values, divided by the sum of squared centered x values. Mathematically, this is $\Sigma[(x_i - \bar{x})(y_i - \bar{y})] / \Sigma(x_i - \bar{x})^2$.

Conditions: The denominator $\Sigma(x_i - \bar{x})^2$ is positive.; The model is simple linear regression with an intercept.