We are looking for the linear regression line parameters (slope and intercept) for variable B (dependent, $Y$) and variable A (independent, $X$).

1. Calculate the means:

• Mean of A ($\bar{x}$) = (1 + 2 + 3 + 4) / 4 = 2.5
• Mean of B ($\bar{y}$) = (5 + 8 + 12 + 15) / 4 = 10

2. Calculate the slope ($b_1$): Using the formula $b_1 = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sum (x_i - \bar{x})^2}$

$\sum (x_i - \bar{x})^2 = (1 - 2.5)^2 + (2 - 2.5)^2 + (3 - 2.5)^2 + (4 - 2.5)^2$ $= (-1.5)^2 + (-0.5)^2 + 0.5^2 + 1.5^2 = 2.25 + 0.25 + 0.25 + 2.25 = 5$

$\sum (x_i - \bar{x})(y_i - \bar{y}) = (-1.5 \times (5 - 10)) + (-0.5 \times (8 - 10)) + (0.5 \times (12 - 10)) + (1.5 \times (15 - 10))$ $= (-1.5 \times -5) + (-0.5 \times -2) + (0.5 \times 2) + (1.5 \times 5)$ $= 7.5 + 1 + 1 + 7.5 = 17$

$b_1 = 17 / 5 = 3.4$

3. Calculate the intercept ($b_0$): $b_0 = \bar{y} - b_1 \times \bar{x}$ $b_0 = 10 - 3.4 \times 2.5 = 10 - 8.5 = 1.5$

The slope is 3.4, and the intercept is 1.5. Thus, Option A is correct.