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This lesson forecasts unemployment rates using a linear regression model and a graphing calculator

Subject:

Math  

Grades:

9, 10, 11, 12  

Title – Line Of Best Fit to Forecast Unemployment Rates
By – James Walsh
Primary Subject – Math
Secondary Subjects – Social Studies
Grade Level – 9-12

Standards Addressed:

    Objective 3 Data Analysis and Probability – NC Competancy Goal 3.03

      Create Linear Models for sets of data to solve problems.

General Goal:

    Create a line of best fit using historical data for unemployment rates.

Specific Objectives:

    Compare the difference between the observed unemployment rates recently and the expected rates based on a linear regression model. Discuss the causes leading to the difference, and discuss whether this trend will continue or return to expected levels.

Required Materials:

  • TI-83 Plus Calculator (or similar)
  • Internet access
  • Historical chart of the unemployment rate over the last twenty years
  • Graph Paper
  • Microsoft Word 2003 or later.

Anticipatory Set (Lead-In):

    Video of the presidential address on the state of the economy

Step-By-Step Procedures:

  1. Show video of presidential address.
  2. Gather historical data from the internet of the unemployment rate over the last 20 years (Bureau of Statistics).
  3. Using data, run a linear regression model.
  4. Compare the observed and expected rates over the last few years leading to the present year.
  5. Graph the differences between the observed and predicted on graph paper.
  6. Have students list five reasons why there is a difference and how it has affected them personally.
  7. Class discussion and sharing
  8. Using model predict the unemployment rates for the next few years. Discuss what changes in society would lower the unemployment rate back to historical levels.

Closure:

    Ask students to list five other types of data one could use linear regression models to help solve problems or make predictions.

Assessment Based On Objectives:

    Using one of the data types students chose, create a linear regression model, and explain how these can be used to solve problems in their own life.

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