11.5 Choose the Best Model for Two-Variable Data

 

Example 1

 

Teacher’s Salaries The table shows the teacher’s salary y (in dollars) for a certain school district, where x is the number of years of teaching experience. Use a graphing calculator to find a model for the data.

 

x

1

2

3

4

y

30,624

32,436

34,167

35,989

 

x

5

6

7

y

37,684

39,311

41,098

 

1.   Make a scatter plot. The points lie approximately on a _line_. This suggests a _linear_ model.

 

 


2.   Use the _linear_ regression feature to find an equation of the model.

 

 


3.   Graph the model along with the data to verify that the model fits the data well.

A model for the data is y = _1739x + 28,950_.


 Deer Population An environmental group observes a deer population in a park where hunting has been banned. The table shows the population y counted x years after the ban began.

x

0

5

10

15

20

y

500

729

1271

2206

3765

 

 

Solution

1.   Make a scatter plot. The points are level at first and then begin to _rise_ rapidly. This suggests an _exponential_ growth model.

2.   Use the _exponential_ regression feature to find an equation of the model.

3.   Graph the model along with the data to verify that the model fits the data well.

 

 


A model for the data is y = _468(1.11)x.

 

 

Example 3

Use a quadratic model

 

Roller Coaster Riders A manager at a local amusement park kept a record of the number of people to ride the most popular roller coaster at the park. The table shows the number of people y that rode the roller coaster x hours after the park had opened. Use a graphing calculator to find a model for the data.

 

 

 

X

0

2

4

6

8

10

12

Y

85

163

282

341

398

381

304

 

Solution

1.   Make a scatter plot. The points form an _inverted U-shape_. This suggests a _quadratic_ model.

2.   Use the _quadratic_ regression feature to find an equation of the model.

3.   Graph the model along with the data to verify that the model fits the data well.

 

 


A model for the data is y _-4.34x2 + 73.7x + 62.8_.