mathalicious

Hitting the Slopes

Can you predict a country's Winter Olympic performance?

Aws4 request&x amz signedheaders=host&x amz signature=139e49782ada9cc172661a608bb3aac47412a192b0f4d25943867a836441587b
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.

Common Core Standards

Content Standards
Mathematical Practices

Downloads

Additional Materials

  • Graphing calculators

Shoutouts

Harry the Otter

Related Lessons

Aws4 request&x amz signedheaders=host&x amz signature=041426582e835c34bc5ab12f574493f2e9f647104703f1740ff39acc9b5e269a

XBOX Xponential

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)
Aws4 request&x amz signedheaders=host&x amz signature=c417f784f529eb68afe188dfb6a5b352757fbcb54d6f6433bf9015413e57d2a4

Joy to the World

What makes for happy countries? Students interpret lines of best fit and correlation coefficients to determine what types of policy changes are most likely to positively impact a country’s well-being.

Topic: Interpreting Categorical and Quantitative Data (ID)
Aws4 request&x amz signedheaders=host&x amz signature=758c73e76c5e0d2761cceb84b9364cbef87e85e137a0f800157364d240f86703

Mercury Rising

How have temperatures changed around the world? Students use periodic functions to compare long-term average monthly temperatures to recorded monthly temperatures, evaluate evidence of climate change, and discuss possible consequences.

Topic: Functions (F), Interpreting Categorical and Quantitative Data (ID), Interpreting Functions (IF)
Aws4 request&x amz signedheaders=host&x amz signature=bfaba8f37feabf73faec98df468d21324e89bff1fa3da82dd86b5ac20d72a0e9

Pic Me

What’s the best way to use Instagram? Students use histograms, linear regressions, and r-squared values to debate the most effective strategies to gain Insta-fame...and the consequences of always having to smile for the camera.

Topic: Interpreting Categorical and Quantitative Data (ID)
Aws4 request&x amz signedheaders=host&x amz signature=8d53388c1a8c8b1e9babf9a3b9ca6a57e06d700083d61d4f9b6c5b98cf6702c3

Win At Any Cost

How should pro sports teams spend their money? Students use linear regressions and r-squared values to analyze data from professional sports leagues, evaluate how various factors correlate with wins, and debate whether a higher payroll is good business strategy.

Topic: Interpreting Categorical and Quantitative Data (ID)