4.7.7: Applications of Linear Interpolation and Extrapolation
- Page ID
- 4335
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Problem Solving with Linear Models
What if you've plotted some data points, with the x−coordinates of the points representing the number of years a teacher has been teaching at a school and the y−coordinates representing his salary? Suppose that you've found the line of best fit to be y=1500x+28,000. Could you use the line of best fit to predict how much his salary will be after he's taught for 12 years? How would you do it?
Linear Modeling
Previously we worked on writing equations and determining lines of best fit. When we fit a line to data using interpolation, extrapolation, or linear regression, it is called linear modeling.
A mathematical model is an equation that describes data. The data may be graphed in a scatter plot.
Let's solve the following problems using linear models:
- Dana heard something very interesting at school. Her teacher told her that if you divide the circumference of a circle by its diameter you always get the same number. She tested this statement by measuring the circumference and diameter of several circular objects. The following table shows her results. From this data, estimate the circumference of a circle whose diameter is 12 inches.
Begin by creating a scatter plot and drawing the line of best fit.
| Object | Diameter (inches) | Circumference (inches) |
|---|---|---|
| Table | 53 | 170 |
| Soda can | 2.25 | 7.1 |
| Cocoa tin | 4.2 | 12.6 |
| Plate | 8 | 25.5 |
| Straw | 0.25 | 1.2 |
| Propane tank | 13.3 | 39.6 |
| Hula hoop | 34.25 | 115 |
Find the equation of the line of best fit using points (0.25, 1.2) and (8, 25.5).
m=(25.5−12)/(8−0.25)=24.3/7.75=3.14 Slope
y=3.14x+b
1.2=3.14(0.25)+b⇒b=0.42
y=3.14x+0.42 Equation
Diameter=12 inches⇒y=3.14(12)+0.42=38.1 inches
In this problem, the slope=3.14. This number should be very familiar to you—it is the number pi rounded to the hundredths place. Theoretically, the circumference of a circle divided by its diameter is always the same and it equals 3.14 or π.
- Using Dana's data from the previous problem, estimate the circumference of a circle whose diameter is 25 inches.
The equation y=3.14x+0.42 of the relationship between diameter and circumference from the previous problem applies here.
Diameter=25 inches⇒y=3.14(25)+0.42=78.92 inches
A circle with a diameter of 25 inches will have a circumference that is approximately 78.92 inches.
- Using Dana's data from the first problem, estimate the circumference of a circle whose diameter is 60 inches.
The equation y=3.14x+0.42 of the relationship between diameter and circumference from the first problem applies here.
Diameter=60 inches⇒y=3.14(60)+0.42=188.82 inches
A circle with a diameter of 60 inches will have a circumference that is approximately 188.82 inches.
Examples
Example 4.7.7.1
Earlier, you were told that you have plotted some data points with the x−coordinates of the points representing the number of years a teacher has been teaching at a school and the y−coordinates representing his salary. Suppose you've found the line of best fit to be y=1500x+28,000. Could you use the line of best fit to predict how much his salary will be after he's taught for 12 years?
Solution
The line of best fit is a linear model that represents the situation. Since you have a linear model, you can plug in 12 for x and solve for y. This would give you the salary of the teacher after he's taught for 12 years.
Example 4.7.7.2
A cylinder is filled with water to a height of 73 centimeters. The water is drained through a hole in the bottom of the cylinder and measurements are taken at two-second intervals. The table below shows the height of the water level in the cylinder at different times.
Solution
| Time (seconds) | Water level (cm) |
|---|---|
| 0.0 | 73 |
| 2.0 | 63.9 |
| 4.0 | 55.5 |
| 6.0 | 47.2 |
| 8.0 | 40.0 |
| 10.0 | 33.4 |
| 12.0 | 27.4 |
| 14.0 | 21.9 |
| 16.0 | 17.1 |
| 18.0 | 12.9 |
| 20.0 | 9.4 |
| 22.0 | 6.3 |
| 24.0 | 3.9 |
| 26.0 | 2.0 |
| 28.0 | 0.7 |
| 30.0 | 0.1 |
Find the water level at 15 seconds.
Begin by graphing the scatter plot. As you can see below, a straight line does not fit the majority of this data. Therefore, there is no line of best fit. Instead, use interpolation.
To find the value at 15 seconds, connect points (14, 21.9) and (16, 17.1) and find the equation of the straight line.
m=(17.1−21.9)/(16−14)=−4.8/2=−2.4
y=−2.4x+b⇒21.9=−2.4(14)+b⇒b=55.5
Equation y=−2.4x+55.5
Substitute x=15 and obtain y=−2.4(15)+55.5=19.5 cm.
Review
- What is a mathematical model?
- What is linear modeling? What are the options to determine a linear model?
- Using the Water Level data, use interpolation to determine the height of the water at 17 seconds.
Use the Life Expectancy table below to answer the questions.
- Make a scatter plot of the data.
- Use a line of best fit to estimate the life expectancy of a person born in 1955.
- Use linear interpolation to estimate the life expectancy of a person born in 1955.
- Use a line of best fit to estimate the life expectancy of a person born in 1976.
- Use linear interpolation to estimate the life expectancy of a person born in 1976.
- Use a line of best fit to estimate the life expectancy of a person born in 2012.
- Use linear extrapolation to estimate the life expectancy of a person born in 2012.
- Which method gives better estimates for this data set? Why?
| Birth Year | Life expectancy in years |
|---|---|
| 1930 | 59.7 |
| 1940 | 62.9 |
| 1950 | 68.2 |
| 1960 | 69.7 |
| 1970 | 70.8 |
| 1980 | 73.7 |
| 1990 | 75.4 |
| 2000 | 77 |
The table below lists the high temperature for the first day of each month in 2006 in San Diego, California (Weather Underground). Use this table to answer the questions.
- Draw a scatter plot of the data.
- Use a line of best fit to estimate the temperature in the middle of the 4th month (month 4.5).
- Use linear interpolation to estimate the temperature in the middle of the 4th month (month 4.5).
- Use a line of best fit to estimate the temperature for month 13 (January 2007).
- Use linear extrapolation to estimate the temperature for month 13 (January 2007).
- Which method gives better estimates for this data set? Why?
| Month number | Temperature (F) |
|---|---|
| 1 | 63 |
| 2 | 66 |
| 3 | 61 |
| 4 | 64 |
| 5 | 71 |
| 6 | 78 |
| 7 | 88 |
| 8 | 78 |
| 9 | 81 |
| 10 | 75 |
| 11 | 68 |
| 12 | 69 |
Mixed Review
- Simplify 6t(−2+7t)−t(4+3t).
- Solve for y:119/8=−10/3(y+(7/5))−(5/2).
- Determine the final cost. Original cost of jacket: $45.00; 15% markup; and 8% sales tax.
- Write as a fraction: 0.096.
- Is this function an example of direct variation? g(t)=−35t+15. Explain your answer.
- The following data shows the number of youth-aged homicides at school during various years.
- Graph this data and connect the data points.
- What conclusions can you make regarding this data?
- There seems to be a large drop in school homicides between 1999 and 2001. What could have happened to cause such a large gap?
- Make a prediction about 2009 using this data.
Year 1993 1995 1997 1999 2001 2003 2005 2007
# 34 28 28 33 14 18 22 30
Review (Answers)
To see the Review answers, open this PDF file and look for section 5.12.
Vocabulary
| Term | Definition |
|---|---|
| linear modeling | When we fit a line to data using interpolation, extrapolation, or linear regression, it is called linear modeling. |
| model | An equation that best describes the data graphed in a scatter plot. |
Additional Resources
Video: Solving Word Problems in Slope-Intercept Form - Example 1
Activity: Problem Solving with Linear Models Discussion Questions
Practice: Applications of Linear Interpolation and Extrapolation