Answer :
Let's figure out what the equation of the line of best fit predicts for [tex]\( x = 3 \)[/tex].
The equation of the line of best fit, given in the problem, is:
[tex]\[ y = 2.5x - 1.5 \][/tex]
We need to find the predicted value of [tex]\( y \)[/tex] when [tex]\( x = 3 \)[/tex].
1. Start by substituting [tex]\( x = 3 \)[/tex] into the equation:
[tex]\[ y = 2.5 \cdot 3 - 1.5 \][/tex]
2. Next, calculate [tex]\( 2.5 \cdot 3 \)[/tex]:
[tex]\[ 2.5 \cdot 3 = 7.5 \][/tex]
3. Now, subtract 1.5 from 7.5:
[tex]\[ y = 7.5 - 1.5 \][/tex]
4. Performing the subtraction gives:
[tex]\[ y = 6 \][/tex]
Therefore, the equation [tex]\( y = 2.5x - 1.5 \)[/tex] predicts that the value of [tex]\( y \)[/tex] when [tex]\( x = 3 \)[/tex] will be [tex]\( 6 \)[/tex]. Thus, the correct answer is:
C. 6
The equation of the line of best fit, given in the problem, is:
[tex]\[ y = 2.5x - 1.5 \][/tex]
We need to find the predicted value of [tex]\( y \)[/tex] when [tex]\( x = 3 \)[/tex].
1. Start by substituting [tex]\( x = 3 \)[/tex] into the equation:
[tex]\[ y = 2.5 \cdot 3 - 1.5 \][/tex]
2. Next, calculate [tex]\( 2.5 \cdot 3 \)[/tex]:
[tex]\[ 2.5 \cdot 3 = 7.5 \][/tex]
3. Now, subtract 1.5 from 7.5:
[tex]\[ y = 7.5 - 1.5 \][/tex]
4. Performing the subtraction gives:
[tex]\[ y = 6 \][/tex]
Therefore, the equation [tex]\( y = 2.5x - 1.5 \)[/tex] predicts that the value of [tex]\( y \)[/tex] when [tex]\( x = 3 \)[/tex] will be [tex]\( 6 \)[/tex]. Thus, the correct answer is:
C. 6
