The table shows the relationship between time spent running and distance traveled. A 2-column table with 5 rows. The first column is labeled time (minutes) with entries 1, 2, 3, 4, 5. The second column is labeled distance (feet) with entries 530; 1,050; 1,600; 2,110; 2,650. Which type of model best describes the relationship? linear, because the r value for the linear model is closest to 1
exponential, because the r value for the exponential model is closest to 0
linear, because the rate of change between each pair of points is exactly 520
exponential, because the rate of change between each pair of points is 1.98

In this question, we have two columns, you giving the time and the other the distance. We have to identify it the relationship is linear or exponential.

First, I am going to explain when we have a linear relation and when we have an exponential relation, then we observe the data and solve this question, getting the following correct

linear, because the r value for the linear model is closest to 1

Linear and exponential data:

If the absolute value of the change assume close values, it is linear.

If the rates are, it is exponential.

Data:

t = 1 -> d = 530

t = 2 -> d = 1050

t = 3 -> d = 1600

t = 4 -> d = 2110

t = 5 -> d = 2650

Subtractions:

2650 - 2110 = 540

2110 - 1600 = 510

1600 - 1050 = 550

1050 - 530 = 520

Divisions(rates):

2650/2110 = 1.25

2110/1600 = 1.32

1600/1050 = 1.52

1050/530 = 1.98

The rates change considerably more than the subtractions, so it is linear relationship, and the correct option is:

linear, because the r value for the linear model is closest to 1.

For more on linear/exponential growths, you can check link

Step-by-step explanation:

It is not perfectly linear because the difference between the y values is not constant. However, when you use the regression function on your calculator and enter the L1 values as your x's and the L2 values as your y's and use the LinReg equation, you get an r-squared value of .999900 and an r value of .999950. So it linear, with your answer being "linear, because the r value for the linear model is closest to 1".

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step-by-step explanation: