In the full lesson
- Slope via Least Squares (formula)
- Why Squared? (text)
- Conceptual Example (example)
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Module 6 · Regression & Correlation · Topic 5
Least Squares Method
Least Squares. The least squares method finds the line that minimizes the sum of the squared residuals (SSE = Σ(yᵢ − ŷᵢ)²). This produces the best-fitting line in terms of vertical distances from data points.
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Key formulas
From the Regression & Correlation formula sheet
- Least-Squares Regression Line:
yhat = a + bx- Line that minimizes the sum of squared residuals. - Pearson Correlation:
r = sum((x_i-xbar)(y_i-ybar)) / sqrt(sum((x_i-xbar)^2)sum((y_i-ybar)^2))- Measures direction and strength of a linear association.
Related glossary terms
- Residual: The observed value minus the predicted value. Example: If y = 12 and yhat = 9, the residual is 3.
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