Old faithful geyser dataset rebooted with Python

dm.jpg by Dane Miller – 4/9/18

Here is a popular dataset on old faithful geyser eruptions in Yellowstone, WY. The dataset comes from Weisberg (2005) publication in Applied Linear Regression. This type of dataset can be extremely useful to National Park Service Rangers for predicting eruptions for visiting tourist. I would highly recommend visiting Yellowstone and seeing old faithful geyser in person it is truly amazing!

Source of the data: http://www.stat.cmu.edu/~larry/all-of-statistics/=data/faithful.dat

Weisberg, S. (2005). Applied Linear Regression, 3rd edition. New York: Wiley, Problem 1.4.

Yellowstone NPS https://www.nps.gov/yell/planyourvisit/exploreoldfaithful.htm

seaborn.jointplot https://seaborn.pydata.org/generated/seaborn.jointplot.html

This dataset contains only two variables duration of the current eruption, and the wait time in between eruptions.

Let’s look at a theoretical model: μ = β0 + β1Xi

μ : Wait time         β1Xi: Duration

Empirical model:  ^yi = b0 +b1xi1

y= observed wait time          b1xi1: observed duration

coef std err t P>|t| [0.025 0.975]
Intercept 35.0774 1.184 29.630 0.000 32.748 37.407
duration_sec 10.7499 0.325 33.111 0.000 10.111 11.389

Wait time = 35.0774 + 10.7499Duration

When I was initially introduced to this dataset in graduate school during a stats course. My focus then was to complete the problems as quickly as possible so that I could get back to my graduate research. However, I missed on some important subtleties in this simply dataset.

Rushing for a dataset in graduate school with Microsoft Excel. Looks pretty crappy! What was I thinking!!!


Plotting the residuals:

The data is separating into two groups.


The same old faithful dataset now using seaborn.jointplot in python.



Focus your efforts on learning python or R it will drastically improve your work. And there you have it a rebooted old faithful dataset plotted with seaborn.jointplot in python.



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