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.
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 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.
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 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
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
60-80 min
Contents
Contents
Teaching Module
(25 min)
A teaching module to explain the basics of scatter plots, correlation coefficients, regression equations, and linear regression
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.
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:
-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.
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 production-based CO2 emissions in China have changed from 1850 (pre-industrial)- 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 production-based CO2 emissions in China from the Twentieth century to recent times (1900-2017)
discuss how these changes suggest that the planet is facing a significant increase in CO2 emissions in last 50 years
This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.
Strictly Necessary Cookies
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.
