A high school math class uses Olympic 100-meter dash data to teach graphing, correlation coefficients, and linear regression for making predictions.
Setting Up the Data Analysis
•Students graph winning times from 1900-1996, deciding on x-axis (years) and y-axis (times) scales.•They create scatter plots, identify the relationship as linear with no curve, and discuss outliers.•Groups estimate correlation coefficients, noting negative values because the line slopes downward as times decrease.Using Technology for Verification and Predictions
•Students enter data into calculators, use Zoom STAT to view scatter plots, and perform linear regression to find exact correlation coefficients.•They apply the regression equation to predict future times, such as 9.54 seconds for 2020 and verifying Usain Bolt's 2012 time.•The class discusses how trends might level off as times approach physical limits, affecting correlation.Real-World Applications and Exit Activity
•Careers that could use this data include athletes, gamblers, shoe companies (e.g., Nike for product design), journalists, commentators, investors, and health professionals.•For an exit ticket, students individually predict the time needed to win gold in 2016 and explain their reasoning, reinforcing prediction skills.Key Takeaways
•Linear regression allows predicting future Olympic sprint times based on historical data trends.•Correlation coefficients indicate the strength and direction of relationships, with negative values here showing decreasing times over years.•This math skill has practical applications in sports, business, and media for forecasting and decision-making.Conclusion
This lesson demonstrates how mathematical modeling can turn historical data into valuable predictions for real-world scenarios.
