Linear Regression Plot
Team Size vs Project Duration
Software development productivity analysis
Output
Python
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
np.random.seed(419)
BG_COLOR = '#ffffff'
TEXT_COLOR = '#1f2937'
n = 50
team_size = np.random.uniform(2, 20, n)
duration = 30 - 1.2 * team_size + np.random.normal(0, 4, n)
duration = np.clip(duration, 5, 35)
df = pd.DataFrame({'Team Size': team_size, 'Duration (weeks)': duration})
fig, ax = plt.subplots(figsize=(10, 6), facecolor=BG_COLOR)
ax.set_facecolor(BG_COLOR)
sns.regplot(
data=df,
x='Team Size',
y='Duration (weeks)',
scatter_kws={'color': '#27D3F5', 'alpha': 0.6, 's': 55, 'edgecolor': 'white', 'linewidths': 0.5},
line_kws={'color': '#F5276C', 'linewidth': 2.5},
ci=95,
ax=ax
)
for collection in ax.collections[1:]:
collection.set_facecolor('#F5276C')
collection.set_alpha(0.15)
corr = np.corrcoef(team_size, duration)[0, 1]
ax.text(0.95, 0.95, 'r = %.3f' % corr, transform=ax.transAxes, fontsize=12,
color='#F5276C', fontweight='bold', va='top', ha='right',
bbox=dict(boxstyle='round,pad=0.3', facecolor='#f8fafc', edgecolor='#e5e7eb'))
ax.set_xlabel('Team Size (developers)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Project Duration (weeks)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Team Size vs Project Duration', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)
ax.tick_params(colors='#374151', labelsize=10)
for spine in ax.spines.values():
spine.set_color('#e5e7eb')
plt.tight_layout()
plt.show()
Library
Matplotlib
Category
Pairwise Data
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