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.
Use this lesson plan to help your students find answers to:
- Use an example to describe linear regression analysis and polynomial regression.
- Use regression analyses to describe how annual production-based emissions of carbon dioxide (CO2) by China have changed over time.
- Discuss reasons for changes in emissions of carbon dioxide (CO2) and their impact on Earth’s climate.
About the Lesson Plan
|Discipline||Mathematics, Data Science|
|Topic(s) in Discipline||Scatter Plots, Correlation Coefficients,
Regression Equations, Linear Regression,
|Climate Topic||Climate and the Atmosphere
Climate Variability Record
|Computer Skills Required||Intermediate|
|A teaching module to explain the basics of scatter plots, correlation coefficients, regression equations, and linear regression|
(14 and 5 min)
|A video micro-lecture to give Introduction to Simple Linear Regression
A video micro-lecture to give Introduction to Polynomial Regression
|Classroom/ Laboratory activity
|A classroom activity - Python Code to apply understanding of linear regression and polynomial regression using a dataset of the annual production-based emissions of carbon dioxide (CO2) by China, measured in million tonnes per year, for the span of 1902-2018.|
Here is a step-by-step guide to using this lesson plan in the classroom/laboratory. We have suggested these steps as a possible plan of action. You may customize the lesson plan according to your preferences and requirements.
|1||Topic introduction and discussion||1. Use the teaching module, ‘Introduction-Linear Regression and Correlation’ by OpenStaxTM, Rice University (for High School level) or ‘Chapter-3: Linear Regression’ provided by Ramesh Sridharan, Massachusetts Institute of Technology (for Undergraduate level), to introduce these topics of basic statistics.
2. Navigate to the sub-sections within the module to the basics of scatter plots, correlation coefficients, regression equations, and linear regression.
3. Use the in-built practice exercises and quizzes to evaluate your students’ understanding of the topics.
|2||Develop the topic further||Use the video micro-lecture, ‘Introduction to Simple Linear Regression’ by dataminingincae, INCAE Business School for a basic introduction to Simple Linear Regression and terms like dependant variable, independent variable, regression line, regression coefficients.
Use the video micro-lecture, ' Polynomial Regression' by Art of Visualization for a basic introduction to Polynomial Regression and how it is useful to fit a nonlinear model to the data.
|3||Extend understanding by practicing Hands-on Python code||1. Use the provided Dataset china-co2-csv.csv and Python Notebook Simple-and-Polynomial-Regression.ipynb.
2. The dataset includes Annual Production-based Emissions of Carbon Dioxide (CO2) by China, measured in million tonnes per year, for the span 1902-2018.
Data Source: Carbon Dioxide Information Analysis Center (CDIAC) and Global Carbon Project
3. Use the Python Notebook and Dataset to:
4. Encourage your students to answer topical questions by applying their understanding of scatter plots, correlation coefficients, linear regression and polynomial regression.
5. Use the regression analyses performed to initiate a discussion on the increase in CO2 emissions from 1980 to 2020 due to anthropogenic activities, which is one major reason behind global climate change.