b. Describe the scatterplot you just created. What do you notice about how the points are placed on the graph? Do you see any patterns?
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c. Place an additional point on your graph for Nate’s car that has an odometer reading of 23,000 miles. Explain your strategy for deciding where to put the point.
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d. When a relationship exists, one way to help show a trend in the data is to place a line or curve that, in general, represents where the data falls. This line, sometimes called a line of best fit, does not need to touch any of the actual data points. Instead, it shows where the data generally falls. The line is a mathematical model of the data. Models of data help you describe the data more easily and help you make predictions for other cars with different mileages. With your team, decide where a line of best fit could be placed that would best model the data points. Are there any limits to where your line makes sense?
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NOTE: Students are eyeballing the line of best fit. They are NOT expected to get the line below. Student answers should be going a downward direction and roughly through the middle of the points.
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Regression Parameters
Statistics
Residuals
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e. Using the line of best fit, can you predict the price of a car with an odometer reading of 80,000 miles? If so, explain how the line of best fit helps. If not, explain why it is not helpful.
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f. Based on the scatterplot, would you agree with Nate’s claim that cars with a higher odometer reading cost less? Use the scatterplot to justify your answer.
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