# data[].attributes.body
Indianapolis Airport

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4. When X is increased, one would think that logically the y axis would increase as well.  However, in the data we found, while x increasing would usually cause y to increase, in some instances y would decrease when x would increase.  This is probably because some of the places that were further away had less of a demand.

# data[].attributes.subject
4. When X is increased, one would think that logically the y axis would increase as well.  However, in the data we found, while x increasing would usually cause y to increase, in some instances y would decrease when x would increase.  This is probably because some of the places that were further away had less of a demand.

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Prediction of the cost of flying 600 miles: $474.04

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Prediction of the cost of flying 600 miles: $474.04

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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? 

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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? 

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428.76-421.64= 7.12 If you were to add 200 miles to our trip it would add 7.12 according to our chart

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1.There doesn’t seem to be a direct linear relationship between distance and airfare. We feel that the cost depends on the destination and how busy it is or how much demand there is to get there.

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1.There doesn’t seem to be a direct linear relationship between distance and airfare. We feel that the cost depends on the destination and how busy it is or how much demand there is to get there.

# data[].attributes.body
Scatterplot

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

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

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R= .3622 It does fit the data.  The data shows a slight increase, as does the line