Snowfall Prediction for Chicago

Project 1 ~ Sept. 2000
8th Grade/Algebra 2
Timber Ridge Middle School, Plainfield, Illinois



Using historical weather data students will predict the upcoming winter's snowfall for the City of Chicago. The project will be completed in the fall quarter, the predictions will be compared with the actual winter's snowfall throughout the winter season. Students will examine the effects of very high or very low snowfalls on the city's budget including the effect on city politics from the surprise blizzard of 1979. The student will use the linear regression features of their graphing calculators to assist them with their prediction. A prize will be given to the individual with the closest prediction to the actual snowfall that is supported by an appropriate analysis.


Online Resources

  1. National Climate Data Center
    Self proclaimed as "'s largest active archive of weather data." A US government organization.
  2. National Weather Service
    The Nation Weather Service's web site. A US government organization.
  3. City of Chicago Department of Streets and Sanitation
    The City of Chicago's web site, Streets and Sanitation Department. Information regarding snow removal within the city.
  4. Additional background information: click here.

Performance Objective(s)

Students will be able to use real data to construct a scatter plot. Students will be able to use the scatter plot data to make a prediction. Students will use a linear regression (least squares) fit to aid in their prediction. Students will be able to explain the difference between a guess and a statistical analysis.


Grading of final report and presentation per a grading rubric.

Closure (questions to consider)

  1. Is it possible to have very accurate historical data, and a very rigorous and exact prediction based on this data, and still be very wrong in your prediction?
  2. Is it possible to do a very bad analysis of the data and still have a good prediction?
  3. Is it better to have a good analysis but be way off on your prediction or to have a poor or sloppy analysis but end up predicting the right amount of snowfall? Explain.
  4. How can you tell the difference between a guess and a statistically sound estimate?

Illinois Learning Standards

Last Updated: 9/20/00