To determine the line of best fit derived from the linear regression analysis given in the results, follow these steps:
1. Understand the Linear Regression Equation: The general formula for a linear regression line is [tex]\( y = ax + b \)[/tex], where:
- [tex]\( a \)[/tex] is the slope of the line.
- [tex]\( b \)[/tex] is the y-intercept.
2. Identify the Given Values from the Problem:
- Slope ([tex]\( a \)[/tex]): -3.1
- Y-intercept ([tex]\( b \)[/tex]): 12.9
3. Formulate the Best Fit Line Equation:
Using the identified values, substitute them into the linear regression equation:
[tex]\[
y = -3.1x + 12.9
\][/tex]
4. Review the Provided Choices:
Compare the derived equation with the options given:
- A. [tex]\( y = -3.1x + 12.9 \)[/tex]
- B. [tex]\( y = -0.994x + 12.9 \)[/tex]
- C. [tex]\( y = 12.9x - 3.1 \)[/tex]
- D. [tex]\( -0.994 = -3.1x + 12.9 \)[/tex]
5. Select the Correct Option:
The line of best fit equation [tex]\( y = -3.1x + 12.9 \)[/tex] matches option A.
Thus, the line of best fit is:
[tex]\[
\boxed{A. \ y=-3.1x+12.9}
\][/tex]
