https://file.aiquickdraw.com/mathbot/file/172624995832649ql2q88.jpg

### (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.

A convenience store manager notices that sales of soft drinks are higher on hotter days. The table provides data on the high temperature (°F) and the number of cans sold. Create a scatter plot, find and graph a linear regression equation, and predict sales at 95°F.

This problem involves calculating the linear regression equation for soft drink sales based on temperature data. It teaches key concepts like creating scatter plots, finding the best-fit line, and making predictions using the equation. Ideal for students in Grades 9-12.

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