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 \(\boxed{\hat{y}=-3.79+0.897 x}\).