Big Ideas Math Integrated I, 2016
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Big Ideas Math Integrated I, 2016 View details
5. Analyzing Lines of Fit
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Exercise 19 Page 197

Practice makes perfect
a To perform a linear regression, we first have to enter the values into lists. Push STAT, choose Edit, and then enter the values in the first two columns.
To do a linear regression we push STAT, scroll right to CALC, and then choose the fourth option in the list, LinReg.

We can see the equation for the line of best fit. y=- 0.2x+20

b We can find the correlation coefficient r on the screen with linear regression results.

Therefore the correlation coefficient is approximately r=- 0.968. This tells us that correlation is both negative and very strong. We know that it is strong because it is extremely close to -1. A correlation of -1 would be a direct correlation explained by a line that goes through all of the points.

c Since the data shows the price in thousands of dollars and mileage in thousands of miles, the slope of - 0.2 means that for every 1 000 miles, a car is decreasing in value by $200. Meanwhile, the y-intercept has no interpretation because a used car cannot have the mileage equal to 0.
d In order to estimate the mileage of a car that costs $15 500 we have to substitute 15.5 for y in the equation for the line of the best fit.
y=- 0.2x+20
15.5=- 0.2x+20
- 4.5=- 0.2 x
22.5=x
x=22.5
This means that the mileage of the car with a price of $15 500 is equal to 22500 mi.
e In order to estimate the price of a car with 6000 miles we have to substitute 6 for x in the equation for the line of the best fit.
y=- 0.2x+20
y=- 0.2( 6)+20
y=- 1.2+20
y=18.8
This means that the price of a car with 6 000 miles is equal to $18 800.