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CA Foundation · Quantitative Aptitude · Correlation and Regression

In a linear regression analysis of annual advertising spend (in ₹ lakhs) and sales revenue (in ₹ crores) for 25 retail outlets, the regression equation is estimated as: Sales = 8.5 + 1.2 × Advertising. If an outlet increases advertising spend from ₹15 lakhs to ₹18 lakhs, by how much would sales revenue be predicted to increase?

When advertising increases by ₹3 lakhs (from 15 to 18), the regression slope of 1.2 indicates sales will increase by 1.2 × 3 = ₹3.6 crores. The regression slope represents the marginal impact per unit change in the independent variable.

  1. A₹3.6 croresCorrect
  2. B₹2.4 crores
  3. C₹28.1 crores
  4. D₹36 lakhs

Explanation

The regression coefficient for Advertising is 1.2, meaning for each additional ₹1 lakh in advertising, sales increase by ₹1.2 crores. The increase in advertising is 18 − 15 = 3 lakhs. Therefore, predicted increase in sales = 1.2 × 3 = 3.6 crores. Option B uses wrong units; option C incorrectly applies the entire equation; option D misinterprets the slope.

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