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5.6: Predicting with Linear Models

Created by: CK-12

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.

Vocabulary

Terms introduced in this lesson:

surveys
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(ax+b) or LinReg(a+bx) 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.

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