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

Teaching Module(25 mins)

1. Use the teaching module, ‘Introduction-Linear Regression and Correlation’ by OpenStax^TM, 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.

For High School

For Undergraduate

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

Video micro-lectures (14 and 5 min)

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

Classroom/ Laboratory activity(30 min)

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. This can be found here.
3. Use the Python Notebook and Dataset to:

• Read the Dataset using DataFrame
• Know the basics of the dataset like its dimensions, data types and memory usage
• Plot the scatter plot of yearly co2 emissions variable
• Use NumPy library to convert the DataFrame to NumPy Array which would be used in the further steps.

Part 1: Linear Regression

• Find the Regression Coefficients  for Simple Linear Regression
• Plot the scatter plot and Regression Line as per the predicted coefficients
• Calculate RMSE (Root Mean-Squared Error-values)
• Discuss how well the Regression Line describes the data points.

Part 2: Polynomial Regression

• Explain how polynomial regression fits a nonlinear model to the data
• Compute the number of output features, then Transform data to polynomial features, fit a Regression for Transformed data, and then predict values
• Calculate RMSE (Root Mean-Squared Error-values)
• Discuss how well the Regression model describes the data points.
• Suggest the students to try different values for the degree of the Polynomial and see the difference between the results visually and also by comparing it using the RMSE value.

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

Use this lesson plan to help your students find answers to:

1. Use the tools and the concepts learned so far to discuss and determine answers to the following questions:
2. Use an example to describe linear regression analysis.
3. Use an example to describe polynomial regression analysis.
4. Determine the difference in the results of linear regression and polynomial regression analyses? What do the results suggest?
5. Use linear and polynomial regression analyses to describe how the annual production-based CO2 emissions in China have changed from 1850 (pre-industrial)- 2018 (last datapoint).
6. Discuss reasons for increasing CO2 emissions and their impact on Earth’s climate.