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Data Science: Linear and Polynomial Regression


As an undergraduate Mathematics or Data Science teacher, you can use this set of computer-based tools to help you in teaching Introductory Statistics and specifically Linear Regression and Polynomial Regression.

This lesson plan will help you to teach Introductory Statistics for Data Science through a Linear Regression and Polynomial Regression assignment. The lesson plan includes a hands-on computer-based classroom activity to be conducted on a dataset of annual production-based emissions of carbon dioxide (CO2) by China, measured in million tonnes per year, for the span of 1902-2018. This activity includes hands-on Python code, a set of inquiry-based questions that will enable your students to apply their understanding of scatter plots, regression equations, correlation coefficients, linear regression, polynomial regression, and the difference between them.

Thus, the use of this lesson plan allows you to integrate the teaching of a climate science topic with a core topic in Mathematics, Statistics, and Data Science.

Learning Outcome

The tools in this lesson plan will enable students to:

  1. Learn about linear regression and correlation
  2. Understand linear regression equations and related terms such as correlation coefficients
  3. Use linear and polynomial regression analyses and  to describe production-based CO2 emissions in China from the Twentieth century  to recent times (1900-2017)
  4. Discuss how these changes suggest that the planet is facing a significant increase in CO2 emissions in last 50 years


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