Activity: The Height Prediction Game - A Linear Regression Adventure

Objective:

Learn about Linear Regression by predicting the height of a tree as it grows!

Materials:

  • A large piece of paper or poster board.

  • Ruler or measuring tape.

  • Markers or colored pencils.

  • Stickers or small cut-out leaves.

  • Data Table of Tree Growth (provided below).

Linear Regression
Linear Regression

Instructions:

  1. Create Your Growth Chart:

    • Draw a big tree on your poster board.

    • Use the ruler to make a vertical line next to the tree, like a giant ruler. Mark feet from 1 to 10.

  2. Plot the Data:

    • Look at the Data Table of Tree Growth.

    • For each year, place a sticker or a leaf cut-out at the height the tree reached that year. For example, in Year 1, put a sticker at 2 feet on your growth chart.

  3. Connect the Dots:

    • Draw a line connecting all the stickers or leaves. This line shows how the tree grew over the years.

  4. Predict the Future:

    • Now, guess how tall the tree will be in Year 9 and Year 10. Place a different colored sticker or leaf where you think it will be.

    • Use the pattern of the line you drew to help you guess. This is like using Linear Regression to predict the future!

  5. Discussion:

    • Talk about your predictions. Why did you choose those heights for Year 9 and Year 10?

    • Explain that in Linear Regression, a computer would use a similar method to make predictions based on past data.

Extra Challenge:

  • Can you predict how tall the tree will be in Year 12? What about Year 18?

  • Discuss how your predictions might change the further away you get from the data you have.

Reflection:

  • Think about other things that grow or change over time. How could you use a chart like this to make predictions about them?

Educational Value:

This activity introduces the concept of Linear Regression in a fun and interactive way. It helps kids understand how patterns in data can be used to make predictions about the future, a fundamental concept in machine learning and AI.