Abstract
From Chapter 4 we have inherited a table of data. Our objective is to analyze the relationship between the temperature and pressure data we collected and see if there are similarities between these measures; and if so, are they meaningful? How can we measure the correlation?
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
On the “Cloud,” which we will introduce in Chapter 6.
- 2.
Imagine if we had only looked at the first 50 rows. At that point the two columns were highly correlated, but by the time we reached 100, the pattern had changed. Perhaps if we went to 150 rows the correlation score would change again? What about 175, or 200?
- 3.
It is likely that you recorded a strong or moderately strong correlation. This is explained by Gay-Lussac’s law, also known as the pressure-temperature law: all other things being equal, if the temperature of a volume of air increases the, pressure increases, and vice versa.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 Philip Meitiner, Pradeeka Seneviratne
About this chapter
Cite this chapter
Meitiner, P., Seneviratne, P. (2020). Introduction to Data Analysis. In: Beginning Data Science, IoT, and AI on Single Board Computers. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-5766-1_5
Download citation
DOI: https://doi.org/10.1007/978-1-4842-5766-1_5
Published:
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-5765-4
Online ISBN: 978-1-4842-5766-1
eBook Packages: Professional and Applied ComputingApress Access BooksProfessional and Applied Computing (R0)