Scatter Plot with Linear Regression — Seaborn

A scatter plot that displays the relationship between two numeric variables with a fitted linear regression line and confidence interval band. This visualization extends the basic scatter plot by adding statistical modeling elements, making it ideal for understanding linear relationships, assessing model fit, and communicating the strength of correlations with visual uncertainty quantification.

Scatter Plot with Linear Regression rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
scatter-regression-linear: Scatter Plot with Linear Regression
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-06
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy import stats


# Theme tokens (see prompts/default-style-guide.md "Background" + "Theme-adaptive Chrome")
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
BRAND = "#009E73"  # Okabe-Ito position 1 — ALWAYS first series

# Configure seaborn theme
sns.set_theme(
    style="ticks",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "grid.color": INK,
        "grid.alpha": 0.10,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

# Data - Temperature vs energy consumption with realistic correlation
np.random.seed(42)
n_points = 100
temperature = np.random.uniform(5, 35, n_points)
energy_consumption = 50 + 2.5 * temperature + np.random.normal(0, 15, n_points)
energy_consumption = np.clip(energy_consumption, 20, 200)

# Calculate regression statistics
slope, intercept, r_value, p_value, std_err = stats.linregress(temperature, energy_consumption)
r_squared = r_value**2
y_pred = slope * temperature + intercept

# Compute 95% confidence interval using scipy
predict_se = np.sqrt(np.sum((energy_consumption - y_pred) ** 2) / (n_points - 2))
confidence_interval = 1.96 * predict_se

# Create figure and axis
fig, ax = plt.subplots(figsize=(16, 9))

# Plot scatter points with moderate transparency
ax.scatter(temperature, energy_consumption, s=180, alpha=0.6, color=BRAND, edgecolors="white", linewidth=1)

# Plot regression line
x_line = np.array([temperature.min(), temperature.max()])
y_line = slope * x_line + intercept
ax.plot(x_line, y_line, color=INK_SOFT, linewidth=3, linestyle="-", label="Linear Fit")

# Plot confidence band
ax.fill_between(x_line, y_line - confidence_interval, y_line + confidence_interval, alpha=0.2, color=BRAND)

# Add regression equation and R² annotation
equation_text = f"y = {slope:.2f}x + {intercept:.1f}\nR² = {r_squared:.3f}"
ax.annotate(
    equation_text,
    xy=(0.05, 0.95),
    xycoords="axes fraction",
    fontsize=18,
    verticalalignment="top",
    color=INK,
    bbox={"boxstyle": "round,pad=0.8", "facecolor": ELEVATED_BG, "alpha": 0.9, "edgecolor": INK_SOFT},
)

# Labels and styling
ax.set_xlabel("Temperature (°C)", fontsize=20, color=INK)
ax.set_ylabel("Energy Consumption (kWh)", fontsize=20, color=INK)
ax.set_title("Temperature vs Energy Consumption", fontsize=24, color=INK, pad=20)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

# Grid styling
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)
ax.set_axisbelow(True)

# Remove top and right spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)

# Set axis limits with padding
ax.set_xlim(0, 38)
ax.set_ylim(10, 210)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
plt.close()

Part of Scatter Plot with Linear Regression on anyplot.ai.

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