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Regression analysis finds the best-fit line (y = mx + b) through a set of data points. It minimizes the sum of squared differences and is fundamental to predictive modeling.
공식
m = (n×Σxy - Σx×Σy) / (n×Σx² - (Σx)²); b = (Σy - m×Σx) / n
단계별 가이드
- 1Input data points (x, y)
- 2Calculate sums: Σx, Σy, Σxy, Σx²
- 3Compute slope m and intercept b using the formula
풀어진 예시
입력
Points: (1,2), (2,4), (3,5)
결과
y ≈ 1.5x + 0.5
Least squares regression line
피해야 할 일반적인 실수
- ✕Confusing correlation with causation
- ✕Not checking for outliers affecting the line
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