13432001

plot.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+df = pd.read_csv("istherecorrelation.csv", sep=";", decimal=",")
+
+beer_per_student = (df.iloc[:, 2] / df.iloc[:, 1])
+
+x = df["Year"].to_numpy(dtype=float)
+y = beer_per_student.to_numpy(dtype=float)
+
+m, b = np.polyfit(x, y, 1)
+y_hat = m * x + b
+
+r2 = 1 - np.sum((y - y_hat)**2) / np.sum((y - y.mean())**2)
+
+plt.figure(figsize=(8,5), dpi=150)
+plt.plot(x, y, marker="o", label="Beer (hl) per 1000 WO students")
+plt.plot(x, y_hat, linestyle="--", label=f"Linear fit (R²={r2:.3f})")
+plt.xlabel("Year")
+plt.ylabel("Hectoliters per 1000 students")
+plt.title("Beer per 1000 WO Students (linear fit)")
+plt.grid(True)
+plt.legend()
+plt.savefig("beer_per_student_linear_fit.png", dpi=300)
+plt.show()

solution_TimonJasarevic.md

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+4. StudentID: 13432001
+
+5. 
+-Fantastic yeasts and where to find them: the hidden diversity of dimorphic fungal pathogens.
+-An analysis of the forces required to drag sheep over various surfaces.
+-The neurocognitive effects of alcohol on adolescents and college students.
+
+![Beer per 1,000 WO Students](beer_per_student_linear_fit.png)
+
+The WO students are drinking less beer over the years. There is a linear correlation with 0.93 R squared.