Skip Navigation

5.6: Predicting with Linear Models

Difficulty Level: At Grade Created by: CK-12
Turn In

Learning Objectives

At the end of this lesson, students will be able to:

  • Collect and organize data.
  • Interpolate using an equation.
  • Extrapolate using an equation.
  • Predict using an equation.


Terms introduced in this lesson:

experimental measurements
non-linear data
linear interpolation
polynomial interpolation
linear extrapolation
most accurate method

Teaching Strategies and Tips

Use the introduction to motivate data collection and organization.

  • Data are gathered from surveys and experimental measurements.
  • Data are organized via tables and scatterplots, where it is easier to spot trends and patterns.

In Example 1:

  • Point out that two data sets are being displayed simultaneously in the scatterplot. This is a common practice when two data sets are being compared.
  • The two variables are Median Age of Males and Females At First Marriage by Year.
  • Discuss with students whether the scatterplot is approximately linear and whether using a line of best fit to predict future values is appropriate. Do the same for Example 2.

Use Examples 3 and 4 to motivate linear interpolation.

  • Ask students how they would go about estimating a value where there is no data point available.
  • Possible discussion questions: Assume the data are linear. How would the line of best fit help? Should only a subset of the data be used? All of the data? How does the above considerations change for non-linear data?

Use Example 5 to motivate linear extrapolation.

  • Point out that the last data point is an outlier and therefore influences the extrapolation heavily.
  • Work through the extrapolation a second time using a linear regression. Have students compare answers from the two models.
  • Emphasize that extrapolation is not useful when used to predict values far into the future (or far into the past).

For additional data sets, visit:

Error Troubleshooting

General Tip: On the TI graphing calculators, students should be using LinReg\begin{align*}(ax+b)\end{align*} or LinReg\begin{align*}(a+bx)\end{align*} to perform linear regressions and not LnReg.

General Tip: Students can neglect to consider the accuracy of a prediction or estimate. Some estimates will not be good because the desired value is far removed from the rest of the data set.

Notes/Highlights Having trouble? Report an issue.

Color Highlighted Text Notes
Show More

Image Attributions

Show Hide Details
Files can only be attached to the latest version of section
Please wait...
Please wait...
Image Detail
Sizes: Medium | Original