A teaching module that demonstrates the use of linear and quadratic regression to analyze Arctic sea ice extent data and the use of graphs, sample correlations, and multiple regression to analyze atmospheric CO2 level data, solar irradiance data, and average global temperature data.
In the first example, the tool describes how students can use linear and quadratic regression for data from 1979 to 2012 to determine trends in the decline of September Arctic sea ice extent.
In the second example, the tool describes how students can use statistical methods such as graphs, sample correlations, and multiple regression to determine the relationship between average global temperature, solar irradiance, and atmospheric CO2 levels for data from 1979 to 2010.
Use this tool to help students find answers to:
- Why is the melting of Arctic ice considered to be a cause and a symptom of climate change?
- According to current models, when will the Arctic start experiencing ice-free summers?
- How much has the rate of reduction of September Arctic sea ice extent changed from 2001 to 2012?
- By applying statistical analysis techniques on the data provided (Ref.: Section 3 (Temperature, Solar Intensity and CO2) of the tool), discuss the relationship between solar irradiance, atmospheric CO2 levels, and global surface temperature.
About the Tool
|Tool Name||Using Data from Climate Science to Teach Introductory Statistics|
|Topic(s) in Discipline||Linear Regression, Quadratic Regression, Multiple Regression, Graphs, Correlation|
|Climate Topic||Climate and the Cryosphere, Climate and the Atmosphere|
|Type of Tool||Teaching Module|
|Location||Arctic Ocean, Global|
|Developed by||Gary Witt, Temple University|
|Hosted at||Journal of Statistics Education|