# data[].attributes.body
Y= 0.2945(1600)+182.44 = $653.64

# data[].attributes.body
y = 0.2945x + 182.44
R² = 0.7789

# data[].attributes.body
The slope is 0.2945. What this means is that for every 1 mile the cost of the mile should be 0.2945.

The y-intercept is 182.44, which means that when x is 0, y is 182.44.

# data[].attributes.body
For r I got 0.882551. It’s almost the same as what I got for r2 on my scatter plot in excel, which was 0.7789. So, I would say that it fit the data line very well.

# data[].attributes.body
No, there appears to be no linear relationship between the distance and airfare ticket. I think there should be a correlation between the two numbers, since the tickets are very expensive. I also feel as though, the price should depend on the miles, so that’s another reason why I think there should be a linear line in between them both.

# data[].attributes.body
I used 1595 (Las Vegas) and 1735 (Puerto Vallarta) that have a distance of about 200. Y=0.2945(1595)+182.44= 652.17, meaning the cost to go to Las Vegas would cost $652.17. Y=0.2945(1735)+182.44= 693.40, which means that the cost to Puerto Vallarta should be $693.40.

# data[].attributes.body
| 10 Different Destinations | Air distance in miles | Cost in $ for airline ticket
| Fort Worth, Texas   | 791.97 miles | $291
| Florence, Italy | 4778.09 miles | $1381
|  Tampa, Florida | 856.18 | $489
|  Puerto Vallarta, Mexico | 1735.33 | 408
| Las vegas, nevada   | 1595.29 miles | 1130
| Chicago, Illinois   | 158.62 miles | 404
| Los Angeles, California   | 1805.53 miles | 751
| London   | 4039.95 | 983
|  Cozumel, Mexico | 1313.56 miles | 395
|  Bora Bora | 5680.98 | 2293