12 points are given on the plane
[tex]\lbrace(1,6),(2,6),(2,5),(2,3),(3,5),(3,2),(4,4),(4,3),(4,1),(5,1),(6,1),(7,1)\rbrace[/tex]
Finding the mean of X and Y,
[tex]\begin{gathered} M_x=\frac{1+2+2+2+3+3+4+4+4+5+6+7}{12}=3.5833 \\ and \\ M_y=\frac{6+6+5+3+5+2+4+3+1+1+1+1}{12}=3.4167 \end{gathered}[/tex]
Then, the sum of squares and the sum of products,
[tex]\begin{gathered} SS_x=\sum_{n\mathop{=}0}^{12}(x_n-M_x)^2=34.9167 \\ and \\ S_{xy}=\sum_{n\mathop{=}0}^{12}(x_n-M_x)(y_n-M_y)=-23.9167 \end{gathered}[/tex]
x_n and y_n are the x and y
In general, the linear regression equation is given by the formula below
[tex]\begin{gathered} y=bX+a \\ b=\frac{S_{xy}}{SS_x} \\ and \\ a=M_y-bM_x \end{gathered}[/tex]
Therefore, in our case, calculating a and b,
[tex]\begin{gathered} b=-\frac{23.9167}{34.9167}=-0.68496 \\ and \\ a=3.4167-(-0.68496)*3.5833=5.87112 \end{gathered}[/tex]
Thus, the line of best fit is
[tex]\Rightarrow y=-0.68496x+5.89112[/tex]
Hence, the option that more resemble the equation above is the last option. The answer is y=-2x/3+6 2/3