Choice A is correct. An equation of a line of best fit for data set F can be written in the form \( y = a + bx \), where \( a \) is the \( y \)-coordinate of the \( y \)-intercept of the line of best fit and \( b \) is the slope. The line of best fit shown for data set E has a \( y \)-intercept at approximately \( (0, 12) \). It’s given that data set F is created by multiplying the \( y \)-coordinate of each data point from data set E by 3.9. It follows that a line of best fit for data set F has a \( y \)-intercept at approximately \( (0, 12(3.9)) \), or \( (0, 46.8) \). Therefore, the value of \( a \) is approximately 46.8. The slope of a line that passes through points \( (x_{1}, y_{1}) \) and \( (x_{2}, y_{2}) \) can be calculated as \( \frac{y_{2} – y_{1}}{x_{2} – x_{1}} \). Since the line of best fit shown for data set E passes approximately through the point \( (12, 30) \), it follows that a line of best fit for data set F passes approximately through the point \( (12, 30(3.9)) \), or \( (12, 117) \). Substituting \( (0, 46.8) \) and \( (12, 117) \) for \( (x_{1}, y_{1}) \) and \( (x_{2}, y_{2}) \), respectively, in \( \frac{y_{2} – y_{1}}{x_{2} – x_{1}} \) yields \( \frac{117 – 46.8}{12 – 0} \), which is equivalent to \( \frac{70.2}{12} \), or 5.85. Therefore, the value of \( b \) is approximately 5.85, or approximately 5.9. Thus, \( y = 46.8 + 5.9x \) could be an equation of a line of best fit for data set F.
Choice B is incorrect and may result from conceptual or calculation errors. Choice C is incorrect and may result from conceptual or calculation errors. Choice D is incorrect. This could be an equation of a line of best fit for data set E, not data set F.
Choice A is correct. An equation of a line of best fit for data set F can be written in the form \( y = a + bx \), where \( a \) is the \( y \)-coordinate of the \( y \)-intercept of the line of best fit and \( b \) is the slope. The line of best fit shown for data set E has a \( y \)-intercept at approximately \( (0, 12) \). It’s given that data set F is created by multiplying the \( y \)-coordinate of each data point from data set E by 3.9. It follows that a line of best fit for data set F has a \( y \)-intercept at approximately \( (0, 12(3.9)) \), or \( (0, 46.8) \). Therefore, the value of \( a \) is approximately 46.8. The slope of a line that passes through points \( (x_{1}, y_{1}) \) and \( (x_{2}, y_{2}) \) can be calculated as \( \frac{y_{2} – y_{1}}{x_{2} – x_{1}} \). Since the line of best fit shown for data set E passes approximately through the point \( (12, 30) \), it follows that a line of best fit for data set F passes approximately through the point \( (12, 30(3.9)) \), or \( (12, 117) \). Substituting \( (0, 46.8) \) and \( (12, 117) \) for \( (x_{1}, y_{1}) \) and \( (x_{2}, y_{2}) \), respectively, in \( \frac{y_{2} – y_{1}}{x_{2} – x_{1}} \) yields \( \frac{117 – 46.8}{12 – 0} \), which is equivalent to \( \frac{70.2}{12} \), or 5.85. Therefore, the value of \( b \) is approximately 5.85, or approximately 5.9. Thus, \( y = 46.8 + 5.9x \) could be an equation of a line of best fit for data set F.
Choice B is incorrect and may result from conceptual or calculation errors. Choice C is incorrect and may result from conceptual or calculation errors. Choice D is incorrect. This could be an equation of a line of best fit for data set E, not data set F.