Please answer part b ### Statistical Analysis and Correlation**Use the given data set to complete parts (a) through (c) below. (Use \( \alpha = 0.05 \))**\[\begin{array}{|c|c|c|c|c|c|c|c|c|c|c|}\hlinex & 10 & 8 & 13 & 9 & 11 & 14 & 6 & 4 & 12 & 7 & 5 \\\hliney & 7.45 & 6.77 & 12.73 & 7.12 & 7.81 & 8.85 & 6.08 & 5.38 & 8.14 & 6.43 & 5.72 \\\hline\end{array}\]*Click here to view a table of critical values for the correlation coefficient.*#### a. Construct a Scatterplot. Choose the correct graph below.Select the scatterplot that corresponds to the given data points.- **Option A:** - This scatterplot shows a non-linear pattern with varying increases and decreases in y-values as x-values increase.- **Option B:** - This scatterplot shows a clear upward trend, with y-values increasing as x-values increase. - **Option C:** - This scatterplot shows a downward trend, with y-values decreasing as x-values increase.- **Option D:** - This scatterplot (marked as correct) shows a general upward trend, indicating a positive association between the variables x and y.#### b. Find the Linear Correlation Coefficient*Find the linear correlation coefficient, \( r \), then determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables.*\[\text{The linear correlation coefficient is } r = \underline{\phantom{000}} \quad \text{(Round to three decimal places as needed.)}\]---By analyzing scatterplot D, it is clear that there is a general positive trend, which hints at a positive linear correlation between the variables x and y. This positive trend suggests that as x increases, y tends to increase as well, albeit with some variability.Note that mathematical computation is required to find the exact value of \( r \), using the statistical formula for the linear correlation coefficient. This value will then be ### Table of Critical Values for the Correlation CoefficientThis table provides the critical values for the Pearson correlation coefficient at significance levels of \(\alpha = 0.05\) and \(\alpha = 0.01\). It is a valuable reference for hypothesis testing when evaluating the strength and significance of the relationship between two variables.| \( n \) | \(\alpha = 0.05\) | \(\alpha = 0.01\) ||--------|------------------|------------------|| 4 | 0.950 | 0.990 || 5 | 0.878 | 0.959 || 6 | 0.811 | 0.917 || 7 | 0.754 | 0.875 || 8 | 0.707 | 0.834 || 9 | 0.666 | 0.798 || 10 | 0.632 | 0.765 || 11 | 0.602 | 0.735 || 12 | 0.576 | 0.708 || 13 | 0.553 | 0.684 || 14 | 0.532 | 0.661 || 15 | 0.514 | 0.641 || 16 | 0.497 | 0.625 || 17 | 0.482 | 0.606 || 18 | 0.468 | 0.590 || 19 | 0.456 | 0.575 || 20 | 0.444 | 0.561 || 25 | 0.396 | 0.505 || 30 | 0.361 | 0.465 || 35 | 0.335 | 0.430 || 40 | 0.312 | 0.402 || 45 | 0.294 | 0.378 || 50 | 0.279 | 0.361 || 60

The data is given as-xy107.4586.771312.7397.12117.81148.8566.0845.38128.1476.4355.72

Use the given data set to complete parts (a) through (c) below. (Use a = 0.05.) 10 8 13 12.73 7.45 6.77 Click here to view a table of critical values for the correlation coefficient. X y a. Construct a scatterplot. Choose the correct graph below. OA Ay 16- 12- 8- 4 ● ** 14 8 12 16 Q 5 OB. Ay 16- 12- 8- 4- 0- 0 9 7.12 8 12 16 11 7.81 BECED 14 8.85 Dia A 16- 12- 8- 4. **** 18 6 6.08 ****** 12 16 Q C 4 5.38 12 8.14 A 16- 12- 8 4 10- 7 6.43 ********* 8 12 16 ROO b. Find the linear correlation coefficient, r, then determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables. The linear correlation coefficient is r=1 (Round to three decimal places as needed) 5 5.7