(b) Linear Regression Equation:

The equation of the regression line is: $y = 16.42x - 621.83$ where $y$ is the number of cans sold and $x$ is the temperature in °F.

(c) Prediction for 95°F:

Using the regression model, the predicted number of cans sold at 95°F is approximately: $937.72 \text{ cans}$

Would you like further details or explanations? Here are a few related questions you might find interesting:

1. How does the slope of the regression line affect the prediction?
2. What is the interpretation of the y-intercept in this context?
3. How can we assess the accuracy of the linear regression model?
4. Could another model (e.g., polynomial) provide a better fit for this data?
5. How would you test the validity of the prediction for 95°F?

Tip: In regression analysis, the coefficient of determination (R²) is a key metric to evaluate how well the model fits the data.