# Generating quadratic data
np.random.seed(42)
X = np.linspace(-10, 10, 100)
Y = X**2 + np.random.normal(0, 10, size=len(X))

# Calculate Pearson correlation coefficient
pearson_corr, _ = pearsonr(X, Y)

m, b = np.polyfit(X, Y, 1)  # Fit a linear regression line

# Scatter plot
fig, ax = plt.subplots()
ax.scatter(X, Y, color=sns.color_palette("hls",24)[14], alpha=.9, label='Data points')
plt.plot(X, m * X + b, color='red', alpha=.6, label='Pearson Correlation Line')
plt.title("X vs. Y (Quadratic Relationship)")
plt.xlabel("X")
plt.ylabel("Y")
plt.legend(loc='upper center')
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.xaxis.set_ticks_position('none') 
ax.yaxis.set_ticks_position('none')