It seems that certain countries are perennial powerhouses in the Winter Games. So, is there a way to use existing data to predict how many medals an individual nation will end up taking home? Two researchers think they may have found a solution.
In this lesson, students use scatterplots and linear regression to examine several variables that may help predict Olympic performance. Are the Winter Games largely decided before the opening ceremonies even start?
Students will
Use a scatterplot to describe the qualitative relationship between two variables
Interpret the results of a linear regression (equation and correlation coefficient) in context
Make predictions about Olympic performance based on regression results
Create scatterplots and best-fit lines from tabular data using technology
Compare regression results for various explanatory variables
Describe the strengths and weaknesses of using simple linear regressions, and examine the predictions of a multiple regression
Before you begin
Students should already be familiar with the basics of regression analysis, including how to use technology to generate a simple linear regression. This lesson is intended as an application of those topics, not an introduction, though it includes a brief review of interpreting scatterplots and regression equations in context.
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Mathalicious lessons provide teachers with an opportunity to teach standards-based math through real-world topics that students care about.
How have video game consoles changed over time? Students create exponential models to predict the speed of video game processors over time, compare their predictions to observed speeds, and consider the consequences as digital simulations become increasingly lifelike.
Topic:
Building Functions (BF), Interpreting Categorical and Quantitative Data (ID), Interpreting Functions (IF), Linear, Quadratic, and Exponential Models (LE), Seeing Structure in Expressions (SSE)