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The predicted value of the response variable \( y \) when the explanatory variable \( x = 57.8 \) is approximately:

\[
y = 62.0
\]

Would you like any further details or have additional questions?

Here are 5 related questions to consider:
1. What is the equation of the regression line from this analysis?
2. How can the coefficient of determination (R²) be interpreted in this context?
3. What are the residuals for each data point, and how can they be used to assess model fit?
4. How does linear regression handle outliers in the data set?
5. How does adding more data points affect the accuracy of the prediction?

**Tip**: When interpreting the slope of a regression line, it shows the average change in the dependent variable for each unit increase in the independent variable.

Run a regression analysis on the following data set with y as the response variable. What is the predicted value of the response variable when the explanatory variable has value 57.8? Round to 1 decimal place.

Perform a linear regression analysis on the provided data set and predict the value of the response variable when the explanatory variable x equals 57.8. Includes step-by-step calculation, rounding to one decimal place.

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