Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary.
\begin{tabular}{l|llllll}
$\boldsymbol{x}$ & 6 & 8 & 20 & 28 & 36 \\
\hline $\boldsymbol{y}$ & 2 & 4 & 13 & 20 & 30
\end{tabular}
A. $\hat{y}=-2.79+0.897 x$
B. $\hat{y}=-3.79+0.801 x$
c. $\hat{y}=-3.79+0.897 x$
D. $\hat{y}=-2.79+0.950 x$

Step 1 ：We are given the following data points: \(x = [6, 8, 20, 28, 36]\) and \(y = [2, 4, 13, 20, 30]\).

Step 2 ：We need to find the equation of the regression line, which is of the form \(\hat{y} = b0 + b1x\), where \(b0\) is the y-intercept and \(b1\) is the slope.

Step 3 ：We calculate the mean of x and y as \(x_{mean} = 19.6\) and \(y_{mean} = 13.8\) respectively.

Step 4 ：We calculate the slope \(b1\) using the formula \(b1 = \frac{\Sigma[(x_i - x_{mean}) * (y_i - y_{mean})]}{\Sigma[(x_i - x_{mean})^2]}\), where \(x_i\) and \(y_i\) are the individual sample points indexed with i. The calculated slope is \(b1 = 0.897\).

Step 5 ：We calculate the y-intercept \(b0\) using the formula \(b0 = y_{mean} - b1 * x_{mean}\). The calculated y-intercept is \(b0 = -3.79\).

Step 6 ：Substituting the calculated values of \(b0\) and \(b1\) into the equation of the regression line, we get \(\hat{y} = -3.79 + 0.897x\).

Step 7 ：Final Answer: The equation of the regression line is \(\boxed{\hat{y}=-3.79+0.897 x}\).