# 4.7.5: Fitting Lines to Data

- Page ID
- 4333

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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)## Fitting Lines to Data

Suppose that each day an ice cream truck driver recorded the high temperature outside and the number of ice cream treats sold. They then entered the data points into a graphing calculator. Do you think that they could find the equation of the line that best fit the data? If so, how would they do it? Could they make predictions based on the equation?

**Fitting Lines to Data**

The real-world situations we have worked with form linear equations. However, most data in life is messy and does not fit a line in slope-intercept form with 100% accuracy. Because of this tendency, people spend their entire career attempting to fit lines to data. The equations that are created to fit the data are used to make predictions, as you will see in the next Concept.

This Concept focuses on graphing scatter plots and using a scatter plot to find a linear equation that will best fit the data.

A **scatter plot** is a plot of all the ordered pairs in a table. This means that a scatter plot is a relation, and not necessarily a function. Also, the scatter plot is ** discrete**, as it is a set of distinct points. Even when we expect the relationship we are analyzing to be linear, we should not expect that all the points would fit perfectly on a straight line. Rather, the points will be “scattered” about a straight line. There are many reasons why the data does not fall perfectly on a line. Such reasons include measurement errors and outliers.

** Measurement error** is the amount you are off by reading a ruler or graph.

An ** outlier** is a data point that does not fit with the general pattern of the data. It tends to be “outside” the majority of the scatter plot.

**Let's make a scatter plot of the following ordered pairs:**

(0, 2), (1, 4.5), (2, 9), (3, 11), (4, 13), (5, 18), (6, 19.5)

Graph each ordered pair on one Cartesian plane.

Notice that the points graphed on the plane above look like they might be part of a straight line, although they would not fit perfectly. If the points were perfectly lined up, it would be quite easy to draw a line through all of them and find the equation of that line. However, if the points are “scattered,” we try to find a line that best fits the data. The graph below shows several potential **lines of best fit**.

You see that we can draw many lines through the points in our data set. These lines have equations that are very different from each other. We want to use the line that is closest to **all** the points on the graph. The best candidate in our graph is the red line, A. Line A is the **line of best fit** for this scatter plot.

**Writing Equations for Lines of Best Fit**

Once you have decided upon your line of best fit, you need to write its equation by finding two points on it and using either:

- Point-slope form;
- Standard form; or
- Slope-intercept form.

The form you use will depend upon the situation and the ease of finding the y−intercept.

**Let's write an equation for the data above that we drew a scatter plot for:**

Using the red line from the graph above, locate two points on the line.

Find the slope: m=(11−4.5)/(3−1)=6.52=3.25.

Then y=3.25x+b.

Substitute (3, 11) into the equation. 11=3.25(3)+b⇒b=1.25

The equation for the line that fits the data best is y=3.25x+1.25.

**Finding Equations for Lines of Best Fit Using a Calculator**

Graphing calculators can make writing equations of best fit easier and more accurate. Two people working with the same data might get two different equations because they would be drawing different lines. To get the most accurate equation for the line, we can use a graphing calculator. The calculator uses a mathematical algorithm to find the line that minimizes the error between the data points and the line of best fit.

**Let's use a graphing calculator to find the equation of the line of best fit for the following data:**

(3, 12), (8, 20), (1, 7), (10, 23), (5, 18), (8, 24), (11, 30), (2, 10).

**Step 1:** Input the data into your calculator. Press **[STAT]** and choose the **[EDIT]** option.

Input the data into the table by entering the x values in the first column and the y values in the second column.

**Step 2:** Find the equation of the line of best fit.

Press **[STAT]** again and use the right arrow to select **[CALC]** at the top of the screen.

Choose option number 4: LinReg(ax+b) and press **[ENTER]**. The calculator will display LinReg(ax+b).

Press **[ENTER]** and you will be given the a and b values.

Here a represents the slope and b represents the y−intercept of the equation. The linear regression line is y=2.01x+5.94.

**Step 3:** Draw the scatter plot.

To draw the scatter plot press **[STATPLOT] [2nd] [Y=]**.

Choose Plot 1 and press **[ENTER]**.

Press the **On** option and choose the Type as scatter plot (the one highlighted in black).

Make sure that the X list and Y list names match the names of the columns of the table in Step 1.

Choose the box or plus as the mark since the simple dot may make it difficult to see the points.

Press **[GRAPH]** and adjust the window size so you can see all the points in the scatter plot.

**Step 4:** Draw the line of best fit through the scatter plot.

Press **[Y=]**.

Enter the equation of the line of best fit that you just found: Y1=2.01X+5.94.

Press **[GRAPH]**.

**Now, let's use a line of best fit to solve the following problem:**

Gal is training for a 5K race (a total of 5000 meters, or about 3.1 miles). The following table shows her times for every other month of her training program. Assume here that her times will decrease in a straight line with time. Find an equation of a line of fit. Predict her running time if her race was in August.

Date |
Month number |
Average time (minutes) |
---|---|---|

January | 0 | 40 |

March | 2 | 38 |

May | 4 | 39 |

July | 6 | 38 |

September | 8 | 33 |

November | 10 | 30 |

Begin by making a scatter plot of Gal’s running times. The independent variable, x, is the month number and the dependent variable, y, is the running time in minutes. Plot all the points in the table on the coordinate plane.

Draw a line of fit. When doing this by eye, there are many lines that look like a good fit, so you just have to use your best judgement.

Choose two points on the line you chose: (0, 41) and (4, 38).

Find the equation of the line, first noticing that one of our points, (0, 41), is the y-intercept. Now, all we need is to find the slope.

m=(38−41)/(4−0)=−3/4

y=−3/4x+41

In a real-world problem, the slope and y−intercept have a physical significance.

Slope=number of minutes/month

Since the slope is negative, the number of minutes Gal spends running a 5K race decreases as the months pass. The slope tells us that Gal’s running time decreases 0.75 minutes per month.

The y−intercept tells us that when Gal started training, she ran a distance of 5K in 41 minutes, which is just an estimate, since the actual time was 40 minutes.

The problem asks us to predict Gal’s running time in August. Since July is assigned to month number 6, August will be month number 7. Substitute x=7 into the line-of-best-fit-equation.

y=−3/4(7)+41=−21/4+41=−21/4+164/4=143/4=35(3/4)

The equation predicts that Gal ran the 5K race in 35.75 minutes.

### Examples

Example 4.7.5.1

Earlier, you were told that an ice cream truck driver recorded the high temperature outside and the number of ice cream treats sold. If they enter the data points into a graphing calculator, can they find the equation of the line that best fit the data? Can they make predictions based on the equation?

**Solution**

Yes, with the graphing calculator, the driver can find the equation of the best fit line using the steps that are outlined in this Concept. Once the driver has the equation, they can plug in any temperature to determine the predict the number of ice cream treats that they will sell for that temperature.

Example \(\PageIndex{1}\)

Make a scatter plot and find the equation of a best fit line for the following set of points: (57, 45) (65, 61) (34, 30) (87, 78) (42, 41) (35, 36) (59, 35) (61, 57) (25, 23) (35, 34).

**Solution**

First we will make a scatter plot:

Next, draw in a line, finding the best fit by eye:

Since the two green points, (34,30) and (25,23), are on the line, we ca use them to write the equation. First, find the slope:

m=(30−23)/(34−25)=7/9

Plugging this into point-slope:

y−30=7/9(x−34)

y−30=(7/9)x−(7/9)⋅(34)

Since (7/9)⋅(3/4)≈26.44

y−30=(7/9)x−26.44

y−30+30=(7/9)x−26.44+30

y=(7/9)x+3.56

### Review

- What is a scatter plot? How is this different from other graphs you have created?
- Define line of best fit.
- What is an outlier? How can an outlier be spotted on a graph?
- What are the two methods of finding a line of best fit?
- Explain the steps needed to find a line of best fit “by hand.” What are some problems with using this method?

In 6-8, draw the scatter plot and find the equation of the line of best fit by hand.

- (32, 43) (54, 61) (89, 94) (25, 34) (43, 56) (58, 67) (38, 46) (47, 56) (39, 48)
- (12, 18) (5, 24) (15, 16) (11, 19) (9, 12) (7, 13) (6, 17) (12, 14)
- (3, 12) (8, 20) (1, 7) (10, 23) (5, 18) (8, 24) (2, 10)

In 9 – 11, for each data set, use a graphing calculator to find the equation of the line of best fit.

- (57, 45) (65, 61) (34, 30) (87, 78) (42, 41) (35, 36) (59, 35) (61, 57) (25, 23) (35, 34)
- (32, 43) (54, 61) (89, 94) (25, 34) (43, 56) (58, 67) (38, 46) (47, 56) (95, 105) (39, 48)
- (12, 18) (3, 26) (5, 24) (15, 16) (11, 19) (0, 27) (9, 12) (7, 13) (6, 17) (12, 14)
- Shiva is trying to beat the samosa eating record. The current record is 53.5 samosas in 12 minutes. The following table shows how many samosas he eats during his daily practice for the first week of his training. Will he be ready for the contest if it occurs two weeks from the day he started training? What are the meanings of the slope and the y−intercept in this problem?

Day |
No. of Samosas |
---|---|

1 | 30 |

2 | 34 |

3 | 36 |

4 | 36 |

5 | 40 |

6 | 43 |

7 | 45 |

- Nitisha is trying to find the elasticity coefficient of a Superball. She drops the ball from different heights and measures the maximum height of the resulting bounce. The table below shows her data. Draw a scatter plot and find the equation. What is the initial height if the bounce height is 65 cm? What are the meanings of the slope and the y−intercept in this problem?

Initial height (cm) |
Bounce height (cm) |
---|---|

30 | 22 |

35 | 26 |

40 | 29 |

45 | 34 |

50 | 38 |

55 | 40 |

60 | 45 |

65 | 50 |

70 | 52 |

- Baris is testing the burning time of “BriteGlo” candles. The following table shows how long it takes to burn candles of different weights. Let's assume it’s a linear relation. We can then use a line to fit the data. If a candle burns for 95 hours, what must be its weight in ounces?

Candle weight (oz) |
Time (hours) |
---|---|

2 | 15 |

3 | 20 |

4 | 35 |

5 | 36 |

10 | 80 |

16 | 100 |

22 | 120 |

26 | 180 |

- The table below shows the median California family income from 1995 to 2002 as reported by the U.S. Census Bureau. Draw a scatter plot and find the equation. What would you expect the median annual income of a Californian family to be in year 2010? What are the meanings of the slope and the y−intercept in this problem?

Year |
Income |
---|---|

1995 | 53,807 |

1996 | 55,217 |

1997 | 55,209 |

1998 | 55,415 |

1999 | 63,100 |

2000 | 63,206 |

2001 | 63,761 |

2002 | 65,766 |

**Mixed Review**

- Sheri bought an espresso machine and paid $119.64 including tax. The sticker price was $110.27. What was the percent of tax?
- What are the means of 4/x=141/98? What are the extremes?
- Solve the proportion in question 17.
- The distance traveled varies directly with the time traveled. If a car has traveled 328.5 miles in 7.3 hours, how many hours will it take to travel 82.8 miles?
- Evaluate t(x)=0.85x when x=6015.

### Review (Answers)

To see the Review answers, open this PDF file and look for section 5.10.

### Vocabulary

Term | Definition |
---|---|

discrete |
Discrete numbers or data are those for which there are only certain values or points. For example, many things can only be measured by integers, such as the number of people. In other words, the number of people is .discrete |

measurement error |
The amount you are off by reading a ruler or graph is called .measurement error |

outlier |
An is a data point that does not fit with the general pattern of the data. It tends to be outlier the majority of the scatter plot.outside |

### Additional Resources

PLIX: Play, Learn, Interact, eXplore: **Fitting Lines to Data**

Video: **Fitting a Line to Data**

Activity: **Fitting Lines to Data Discussion Questions**

Study Aid: **Determining the Equation of a Line Study Guide**

Practice: **Fitting Lines to Data**

Real World Application: **Cool Blue Planet?**