Decide which of the two given functions is linear and find its equation.

x	0	1	2	3	4
f(x)	8	13	18	23	28
g(x)	10	13	20	21	28

2. Decide which of the two given functions is linear, and find its equation. HINT [See Example 1.]

x	10	0	10	20	30
f(x)	1.5	0	1.5	2.5	3.5
g(x)	9	4	1	6	11

The table gives the approximate economic value associated with various levels of oil recovery in a U.S. state.

Percent Recovery (%)	10	40	50	80
Economic Value ($ billions)	400	1,100	1,200	2,200

Find the regression line.d(r) =

Use the regression line to estimate the economic value (in billions of dollars) associated with a recovery level of 70%.$ billion

Use correlation coefficients to determine which of the given sets of data is best fit by its associated regression line and which is fit worst. HINT [See Example 3.] (a)

{(5, 7), (6, 8), (9, 10)}

(b)

{(4, 3), (6, 5), (7, 8)}

(c)

{(8, 1), (9, 9), (4, 4)}

Select the data set with the best fit.

{(5, 7), (6, 8), (9, 10)}

{(4, 3), (6, 5), (7, 8)}

{(8, 1), (9, 9), (4, 4)}

Select the data set with the worst fit.

{(5, 7), (6, 8), (9, 10)}

{(4, 3), (6, 5), (7, 8)}

{(8, 1), (9, 9), (4, 4)}

Is it a perfect fit for any of the data sets? Yes No

Use correlation coefficients to determine which of the given sets of data is best fit by its associated regression line and which is fit worst.Select the data set with the best fit.

{(0, 2), (3, 8), (1, 0)}

{(1, 0), (0, 1), (1, 0)}

{(1, 1), (4, 6), (1, 1.1)}

Select the data set with the worst fit.

{(0, 2), (3, 8), (1, 0)}

{(1, 0), (0, 1), (1, 0)}

{(1, 1), (4, 6), (1, 1.1)}

Is it a perfect fit for any of the data sets?YesNo

{(1, 1), (4, 6), (1, 1.1)}

{(1, 0), (0, 1), (1, 0)}

{(0, 2), (3, 8), (1, 0)}

Oil Recovery A U.S. state's Bureau of Economic Geology published a study on the economic impact of using carbon dioxide enhanced oil recovery (EOR) technology to extract additional oil from fields that have reached the end of their conventional economic life. The following table gives the approximate number of jobs for the citizens that would be created at various levels of recovery.

Percent Recovery (%)	20	40	80	100
Jobs Created (Millions)	5	8	11	17

Find the regression line.j(r) =

Use the regression line to estimate the number of jobs (in millions) that would be created at a recovery level of 70%. million jobs