Linear Regression
Gujarat Board · Class 12 · Statistics
Quick revision notes for Linear Regression — Gujarat Board Class 12 Statistics. Key concepts, formulas, and definitions for last-minute revision.
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Key Topics to Revise
1. Core Concepts: Regression vs. Correlation
- Correlation measures the strength and direction of association between two variables but cannot predict values.
- Regression establishes a mathematical (functional) relationship that enables prediction of one variable from another.
- The term 'regression' was coined by Sir Francis Galton from his study of heights of 1000 father-son pairs — sons' heights 'regressed' (returned) toward the mean.
2. Fitting the Regression Line – Method of Least Squares
- Two methods exist: (1) Method of Scatter Diagram — quick but subjective; (2) Method of Least Squares — objective and gives the best-fit line.
- The scatter diagram method is subjective because different people draw different lines, giving different estimates for the same X value.
- The Method of Least Squares finds the line Ŷ = a + bX that minimises the sum of squared vertical errors: Σ(Yi − Ŷi)² = minimum.
3. Step-by-Step Worked Examples
- EXAMPLE 1 (Basic – from raw data): Find the regression line of Y on X for the data: X: 1, 2, 3, 4, 5 and Y: 3, 5, 4, 6, 7
- Step 1: Set up the calculation table.
- Step 2: Compute all required sums: n, ΣX, ΣY, ΣX², ΣXY.
4. Regression Coefficient – Properties and Interpretation
- The regression coefficient b represents the estimated change in Y for a one-unit increase in X.
- If b > 0: Y increases as X increases (positive relationship). If b < 0: Y decreases as X increases (negative relationship).
- Sign of b is always the same as the sign of correlation coefficient r (since σX and σY are always positive).
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