To predict the number of ice cream cones sold on a day with a temperature of 106 degrees, it’s essential to analyze the given data points and find the line of best fit. However, as I don't have the data points you mentioned, I can guide you through the process of doing it yourself.
Here’s a general approach to find a line of best fit:
Steps to Find the Line of Best Fit
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Collect Data: Ensure you have pairs of data representing temperatures and corresponding ice cream sales.
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Plot the Data: Create a scatter plot of the temperature (x-axis) versus the number of ice cream cones sold (y-axis).
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Calculate the Line of Best Fit: You can use the least squares method or software (like Excel, Google Sheets, or statistical software) to compute the line of best fit, represented by the equation: \[ y = mx + b \] where \( m \) is the slope and \( b \) is the y-intercept.
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Use the Equation to Predict: Once you have the equation, substitute \( x = 106 \) (the temperature) into the equation to find \( y \) (the predicted number of ice cream cones sold).
Example Calculation
For example, let's say your line of best fit equation ends up being: \[ y = 2x - 50 \]
You would substitute 106 for \( x \): \[ y = 2(106) - 50 \] \[ y = 212 - 50 \] \[ y = 162 \]
This means on a day with a temperature of 106 degrees, you could expect to sell approximately 162 ice cream cones.
Conclusion
If you provide me with the specific data, I could help you calculate the line of best fit directly. Otherwise, you can follow the steps above to make the prediction yourself!