diff --git a/myplot.png b/myplot.png
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diff --git a/plot.py b/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=";")
+df["WO [x1000]"] = df["WO [x1000]"].str.replace(",", ".").astype(float)
+
+x = df["WO [x1000]"]            
+y = df["NL Beer consumption [x1000 hectoliter]"]  
+
+plt.figure(dpi=300)
+plt.scatter(x, y, color="blue", label="Data points")
+coeffs = np.polyfit(x, y, 1)   
+poly_eq = np.poly1d(coeffs)
+plt.plot(x, poly_eq(x), color="red", label=f"Fit line: y={coeffs[0]:.2f}x+{coeffs[1]:.2f}")
+plt.xlabel("WO (x1000 students)")
+plt.ylabel("Beer consumption (x1000 hectoliter)")
+plt.title("Relationship between WO and Beer consumption in NL")
+plt.legend()
+plt.savefig("myplot.png")
+plt.show()
+
+
+r = df["WO [x1000]"].corr(df["NL Beer consumption [x1000 hectoliter]"])
+print("Correlation coefficient r =", r)
+
+
diff --git a/solution_.md b/solution_.md
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+# Solution
+
+**StudentID:** 16092228
+
+## Papers
+- MCC Van Dyke et al., 2019  
+- JT Harvey, Applied Ergonomics, 2002  
+- DW Ziegler et al., 2005
+
+## Plot and Correlation Analysis
+
+![My Plot](myplot.png)
+
+To examine the relationship between the number of university students (WO) and beer consumption in the Netherlands, 
+we created a scatter plot and calculated the **Pearson correlation coefficient (r)**.
+
+The computed value is:
+
+**r = 0.818**
+
+Since |r| > 0.8, this indicates a **strong positive correlation** between WO and beer consumption.  
+In other words, as the number of university students increases, the data also shows an increase in beer consumption.
+
+However, this relationship may be just coincidental.  
+The observed correlation might be influenced by other factors or simply reflect parallel trends in the data.