# data[].attributes.headline
4. Interpret the slope of your regression line from question 3 in context.

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4. Interpret the slope of your regression line from question 3 in context.

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Slope: 0.085
Y-intercept: 176.58

# data[].attributes.headline
6. Use Excel to produce r. How well does the line fit the data? Explain.

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6. Use Excel to produce r. How well does the line fit the data? Explain.

# data[].attributes.body
= .96
It fits pretty well because there was no major change in the number. All of the numbers are generally pretty close to the regression line and the line wouldn't have moved much.

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5. If 2 cities are about 200 miles apart, then based on your regression line equation, by how much should their airfares differ? Show your work.

# data[].attributes.subject
5. If 2 cities are about 200 miles apart, then based on your regression line equation, by how much should their airfares differ? Show your work.

# data[].attributes.body
193.58= 0.085(200)+176.58
210.58= 0.058(400)+176.58
210.58-193.58= 17

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1. From the numbers in your table, does there appear to be a linear relationship between distance and airfare? Explain.

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1. From the numbers in your table, does there appear to be a linear relationship between distance and airfare? Explain.

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There is not a linear relationship between the distance and airfare. Distances that are pretty close in milage differ in amounts around $200.

# data[].attributes.headline
December 15th through December 21st, 2017

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December 15th through December 21st, 2017

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7. Use the equation of your regression line from question 3 to predict the cost of flying 1600 miles.

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7. Use the equation of your regression line from question 3 to predict the cost of flying 1600 miles.

# data[].attributes.body
= 305