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19 changes: 19 additions & 0 deletions correlation.py
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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

db = pd.read_csv("istherecorrelation.csv", sep=';')


fig, ax = plt.subplots(dpi=300)
ax.plot(db['WO [x1000]'], db['NL Beer consumption [x1000 hectoliter]'])

ax.set(xlabel='WO [x1000]', ylabel='NL Beer consumption [x1000 hectoliter]',
title='WO vs Beer consumption in the Netherlands')
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(6)
ax.grid()

fig.savefig("correlation.png")
plt.show()
19 changes: 19 additions & 0 deletions solution_.md
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# Titles

1. MCC Van Dyke et al. (which one? :) )
- Fantastic yeasts and where to find them: the hidden diversity of dimorphic fungal pathogens
- The rise of coccidioides: forces against the dust devil unleashed

2. JT Harvey, Applied Ergonomics, 2002
- An analysis of the forces required to drag sheep over various surfaces

3. DW Ziegler et al., 2005
- Correlation of continuous cardiac output measured by a pulmonary artery catheter versus impedance cardiography in ventilated patients

# Correlation

As visible in the image below, there is slight correlation between WO and Dutch beer consumption, as both values increase simultaneously.

![Correlation](correlation.png)

However, the correlation is flimsy at best, as WO-values of 205-240 show no increase in beer consumption.