The linear regression equation for the data is:

$y = 70.568 + 0.428x$

a) The percentage of the variation explained by the regression line is:

$R^2 \times 100 = 57.83\% \approx 58\%$

b) Using the equation to predict $y$ when $x = 44$:

$y = 70.568 + 0.428(44) = 89.397$

Would you like more details or have any questions?

Here are 5 related questions to expand on this:

1. How is the slope of a regression line interpreted?
2. What does the R-squared value represent in a linear regression model?
3. How would you calculate the residuals for this regression model?
4. How can outliers affect the regression line?
5. What is the significance of the intercept in a regression model?

Tip: Always check the assumptions of linear regression (linearity, homoscedasticity, normality of residuals, etc.) before relying on the model for predictions.