Linear Regression Plot

Team Size vs Project Duration

Software development productivity analysis

Output
Team Size vs Project Duration
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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