As an undergraduate Mathematics or Data Science teacher, you can use this set of computerbased tools to help you in teaching Introductory Statistics and specifically Linear Regression and Polynomial Regression.
Introduction
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 handson computerbased classroom activity to be conducted on a dataset of annual productionbased emissions of carbon dioxide (CO2) by China, measured in million tonnes per year, for the span of 19022018. This activity includes handson Python code, a set of inquirybased 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.
Questions
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 productionbased 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
Grade Level  Undergraduate 
Discipline  Mathematics, Data Science 
Topic(s) in Discipline  Scatter Plots, Correlation Coefficients, Regression Equations, Linear Regression, Polynomial Regression 
Climate Topic  Climate and the Atmosphere Climate Variability Record 
Location  Global 
Language(s)  English 
Access  Online, Offline 
Computer Skills Required  Intermediate 
Approximate Time Required 
6080 min 
Contents
Contents
Teaching Module
(25 min) 
A teaching module to explain the basics of scatter plots, correlation coefficients, regression equations, and linear regression 
Video microlectures
(14 and 5 min) 
A video microlecture to give Introduction to Simple Linear Regression
A video microlecture to give Introduction to Polynomial Regression 
Classroom/ Laboratory activity
(30 min) 
A classroom activity  Python Code to apply understanding of linear regression and polynomial regression using a dataset of the annual productionbased emissions of carbon dioxide (CO2) by China, measured in million tonnes per year, for the span of 19022018. 
Video
Here is a stepbystep 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, ‘IntroductionLinear Regression and Correlation’ by OpenStax^{TM}, Rice University (for High School level) or ‘Chapter3: Linear Regression’ provided by Ramesh Sridharan, Massachusetts Institute of Technology (for Undergraduate level), to introduce these topics of basic statistics.
2. Navigate to the subsections within the module to the basics of scatter plots, correlation coefficients, regression equations, and linear regression. 3. Use the inbuilt practice exercises and quizzes to evaluate your students’ understanding of the topics. 
2  Develop the topic further  Use the video microlecture, ‘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 microlecture, ' 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 Handson Python code  1. Use the provided Dataset chinaco2csv.csv and Python Notebook SimpleandPolynomialRegression.ipynb.
2. The dataset includes Annual Productionbased Emissions of Carbon Dioxide (CO2) by China, measured in million tonnes per year, for the span 19022018. 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. 
Suggested questions/assignments for learning evaluation :
 Use the tools and the concepts learned so far to discuss and determine answers to the following questions:
 Use an example to describe linear regression analysis.
 Use an example to describe polynomial regression analysis.
 Determine the difference in the results of linear regression and polynomial regression analyses? What do the results suggest?
 Use linear and polynomial regression analyses to describe how the annual productionbased CO2 emissions in China have changed from 1850 (preindustrial) 2018 (last datapoint).
 Discuss reasons for increasing CO2 emissions and their impact on Earth’s climate.
The tools in this lesson plan will enable students to:
 learn about linear regression and correlation
 understand linear regression equations and related terms such as correlation coefficients
 use linear and polynomial regression analyses and to describe productionbased CO2 emissions in China from the Twentieth century to recent times (19002017)
 discuss how these changes suggest that the planet is facing a significant increase in CO2 emissions in last 50 years
1  Teaching Module, “Introduction Linear Regression and Correlation”  Provided by OpenStax^{TM}, Rice University 
2  Teaching Module, “Chapter 3: Linear Regression”  Provided by Ramesh Sridharan, MIT from ‘Statistics for Research Projects’ 
3  Video microlecture, ‘Introduction to Simple Linear Regression’  by dataminingincae, INCAE Business School 
4  Video microlecture, 'Polynomial Regression'  by Art of Visualization 
5  Dataset annual productionbased emissions of carbon dioxide (CO2) by China, measured in million tonnes per year, for the span of 19022018. 
Carbon Dioxide Information Analysis Center (CDIAC) and Global Carbon Project

Python Notebook
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