The line of best fit is described by the equation ŷ = bX + a, where b is the slope of the line and a is the intercept (i.e., the value of Y when X = 0).
We can use the least-squares method to find the line of best fit for a set of paired data, allowing you to estimate the value of a dependent variable (Y) from a given independent variable (X).
To do this we would require the following parameters.
Sum of X
Sum of Y
Mean X
Mean Y
Sum of squares (SSX)
Sum of products (SP)
STEP 1: We compute the mean of the x and y values
STEP 2: we compute the difference of each variable from their respective mean and sum the values the find the sum of squares (SSX) and Sum of products (SP)
The regression equation is given as
[tex]RegressionEquation=ŷ=bX+a[/tex]Where b is
[tex]b=\frac{SP}{SS_X}=\frac{-137.2}{140.8}=-0.97443[/tex]and a is
[tex]a=M_Y-bM_X=10.8-(-0.97\times6.8)=17.42614[/tex]Therefore the line of best fit
[tex]ŷ=-0.974x+17.426[/tex]ANSWER= OPTION B
