Empirical Project 1 Measuring climate change

Learning objectives

In this project you will:

  • use charts and summary measures to discuss the extent of climate change and its possible causes
  • use line charts to describe the behaviour of real-world variables over time (Part 1.1)
  • summarize data in a frequency table, and visualize distributions with column charts (Part 1.2)
  • describe a distribution using mean and variance (Part 1.2)
  • use scatterplots and the correlation coefficient to assess the degree of association between two variables (Part 1.3)
  • explain what correlation measures and the limitations of correlation (Part 1.3).

Key concepts

  • Concepts needed for this project: mean, median, and decile.
  • Concepts introduced in this project: variance, frequency table, correlation and correlation coefficient, causation, and spurious correlation.


CORE projects

This empirical project is related to material in:

Climate change is one of the effects of the rapid economic growth that has occurred in most countries since the Industrial Revolution. It is an important issue for policymaking, since governments need to assess how serious the problem is and then decide how to mitigate it.

To find out more about climate change and its effects, visit the Met Office’s webpage.

Suppose you are a policy advisor for a small island nation. The government would like to know more about the extent of climate change and its possible causes. They ask you the following questions:

  1. How can we tell whether climate change is actually happening or not?
  2. If it is real, how can we measure the extent of climate change and determine what is causing it?

To answer the first question, we look at the behaviour of environmental variables over time to see whether there are general patterns in environmental conditions that could be indicative of climate change. In this project, we focus on temperature-related variables.

To answer the second question, we examine the degree of association between temperature and another variable, CO2 emissions, and consider whether there is a plausible relationship between the two, or whether there are other explanations for what we observe.

Working in Excel

Working in R

Working in Google Sheets

Working in Python