Instructions

Overview:

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.

Details:

• Snowfall data at O'Hare, by month, from 1961 to 1996 is provided. Click here to view the data.
  
  
• Using the Internet resources provided, obtain any additional historical snowfall data for Chicago that you think is needed or useful. Explain your reasons for using some or all of the provided data and for obtaining additional data (if you did) in your report.
  
  
• Analyze the historical data. The minimum analysis should include a linear regression and the measures of central tendency (mean, median, mode). Exactly which data will be analyzed is up to the student, explain your reasons for your data selection in your report.
  
  
• Develop a written report that includes your historical weather data, your prediction for this winter's snowfall in Chicago by month, and the reason(s) your data supports your predictions. Include a discussion of the benefits of a good snowfall prediction to the city's Street Department and other government offices, and the problems that a bad prediction may cause to these entities.
  
  
• Prepare a poster that summarizes your findings and predictions. Use graphs, tables, pictures, text...whatever best conveys your findings and message. The posters will be placed in the classroom to allow everyone to track the prediction vs. actual snowfall throughout the winter. Some means for updating the charts with actual snowfall data each month should be provided.
  
  
• Note that all reported Chicago snowfalls will be that officially registered at O'Hare International Airport. The students report should consider whether this is significant or not to the City of Chicago's budget plans for snow removal.
  
  
• Present your findings to the class (approximately 10 minutes).

Online Resources

1. National Climate Data Center
   Self proclaimed as "...world'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.

Assessment

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

• 8.B.3 Use graphing technology and algebraic methods to analyze and predict linear relationships and make generalizations from linear patterns.
• 10.A.3a Construct, read and interpret tables, graphs (including circle graphs) and charts to organize and represent data.
• 10.A.3c Test the reasonableness of an argument based on data and communicate their findings.
• 10.B.3 Formulate questions (e.g., relationships between car age and mileage, average incomes and years of schooling), devise and conduct experiments or simulations, gather data, draw conclusions and communicate results to an audience using traditional methods and contemporary technologies.