# Analyzing Continuous Data - Climate Variability

Sarah Rubinfeld
,
Carthage College
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#### Summary

The exercise focuses on the analysis of continuous data as applied to climate variability. Using data freely available on the internet, students work in Excel to import their data, organize it, and analyze it using basic statistical tools. In the process they gain familiarity with Excel spreadsheets and with the use, application, and interpretation of linear regression.

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## Context

#### Audience

This activity was developed for an environmental science case studies course for first and second year undergraduates, but would be appropriate for any earth or environmental science course which includes basic statistics and/or data analysis.

#### Skills and concepts that students must have mastered

A very basic familiarity with Excel spreadsheets and charts.
An introduction to correlation and linear regression.

#### How the activity is situated in the course

Developed as a laboratory exercise, but could also be adapted for use in class or as a homework assignment.

## Goals

#### Content/concepts goals for this activity

Learn new ways to use Excel and gain experience with those already known

#### Higher order thinking skills goals for this activity

Practice statistical techniques used to analyze continuous data
Evaluate how the earth's climate has varied over the past 200 years

#### Other skills goals for this activity

Explore the benefits and challenges of working with other people's data
Apply presentation skills to a spreadsheet format

## Description of the activity/assignment

The activity is divided into seven parts, as follows:
Part A: students access an online data set of historic global temperature anomalies and use the webpage to answer questions about the source and presentation of the data.
Part B: students copy the data into an Excel spreadsheet and organize it so that it is easy for them to use and for others to follow.
Part C: students graph their data, explore the use of trend lines, and use a linear regression line to predict future temperatures.
Part D: students access an online data set of historic temperature anomalies within their latitude zone, analyze this data, and compare their results to those from Part C.
Part E: students access an online data set of historic temperatures for their state, analyze this data, and compare their results to those from Parts C and D.
Part F: students choose two original questions related to climate variability and use these or other data sets to address their questions.
Part G: students evaluate the statistical significance of their linear regression lines and interpret their results in the context of climate variability

## Determining whether students have met the goals

The students submit Excel spreadsheets which include data, graphs, and written answers to questions. They are evaluated on the quality of their statistical analysis, the clarity of their worksheets, and their ability to interpret their results.