We will have the following:
*Firts: We have that the correlation coefficient is given by:
![r=\frac{\sum ^n_(i=1)X_iY_i-(1)/(n)(\sum ^n_(i=1)X_i)(\sum ^n_(i=1)Y_i)}{\sqrt[]{\sum^n_(i=1)X^2_i-(1)/(n)(\sum^n_(i=1)X_i)^2}\sqrt[]{\sum ^n_(i=1)Y^2_i-(1)/(n)(\sum ^n_(i=1)Y_i)^2}}](https://img.qammunity.org/2023/formulas/mathematics/college/715bptp0gwzaqs2h3ztx2ek844drah3nza.png)
*Second: We calculate the means, that is:

&

*Third: We calculate the sums:



Fourth: We replace the data:
![r=\frac{-5.16}{\sqrt[]{17.2\cdot1.588}}\Rightarrow r=-0.987](https://img.qammunity.org/2023/formulas/mathematics/college/kirh69r0jv2uvqofc5pfcgk682yllb5l7q.png)
Thus making the coefficient r = -0.987.
And this would be the scatterplot: