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Statistical Exploration of Climate Data

By Tamra Carpenter, Robert Vanderbei, Jon Kettenring

Published on

Abstract

This resource has been updated - find the current version here: https://qubeshub.org/qubesresources/publications/900


Teacher version       Student version
This module is appropriate for introductory statistics classes.

Supporting files: 
Module figures for teacher presentation 
Data Sets 
Fairbanks.dat.txt  getDIMACSdata.sh.txt  McGuireAFB.dat.txt  NewOrleans.dat.txt Raleigh.dat.txt 


Authors: Tamra Carpenter, Robert Vanderbei, Jon Kettenring 
Module Summary: In this module the students will learn some basic concepts in statistical thinking about data, with emphasis on exploratory data analysis. The module will analyze daily temperature data collected over 55 years at a single location. The analysis explores the question, 4Is there any observable temperature trend over this time period at this location?! The challenge is to see a potentially small change within a data set that has both seasonal variability and high daily variability. Basic plots are done to help the students view data in different ways, introduce methods for removing seasonality, and use averaging to reduce day-to-day variability. This module might be viewed as a case study! in data analysis. It will give students a taste of what it's like to do real world data analysis. Students will work with a large noisy data set and look at it in different ways to try to answer a specific question.

Cite this work

Researchers should cite this work as follows:

  • Carpenter, T., Vanderbei, R.,  Kettenring, J. "Statistical Exploration of Climate Data." DIMACS, Rutgers University: New Brunswick, NJ.  Retrieved March 11, 2015

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