Run a regression analysis on the following bivariate set of data with \(y\) as the response variable.

\[
\begin{array}{cc}
x & y \\
70 & 69.5 \\
51.9 & -21.7 \\
58.1 & 39.1 \\
67.4 & 74.9 \\
95 & 156.2 \\
70.7 & 97.6 \\
62.9 & 89 \\
50.4 & 16.8 \\
60.9 & 37.4 \\
49 & 29.1 \\
61.4 & 59.6 \\
60.3 & 35.1 \\
\end{array}
\]

1. Find the correlation coefficient and report it accurate to three decimal places.

\[
r =
\]

2. What proportion of the variation in \(y\) can be explained by the variation in the values of \(x\)? Report the answer as a percentage accurate to one decimal place. (If the answer is 0.84471, then it would be 84.5%. You would enter 84.5 without the percent symbol.)

\[
r^2 = \%
\]

3. Based on the data, calculate the regression line (each value to three decimal places).

\[
y = \ x +
\]

4. Predict what value (on average) for the response variable will be obtained from a value of 49.2 as the explanatory variable. Use a significance level of \(\alpha = 0.05\) to assess the strength of the linear correlation. What is the predicted response value? (Report answer accurate to one decimal place.)

\[
y =
\]