Here are the results based on the data provided:

1. Correlation coefficient: $-0.8968$
2. Regression equation: $y = -0.116x + 57.085$ (rounded to three decimal places)
3. Predicted mpg for a car with 236 horsepower: $29.80$ (rounded to two decimal places)

Would you like more details or explanations?

Here are 5 related questions to deepen understanding:

1. How does a negative correlation coefficient affect the relationship between horsepower and mpg?
2. What does the slope of the regression line represent in the context of this data?
3. How can you use the regression equation to predict mpg for other horsepower values?
4. What is the significance of rounding values in statistical analysis?
5. Why is it important to check the p-value when interpreting linear regression results?

Tip: The closer the correlation coefficient is to $-1$ or $1$, the stronger the linear relationship between the two variables.