Scatter Plot with Linear Regression — Pygal

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 Pygal

Python source (Pygal)

""" anyplot.ai
scatter-regression-linear: Scatter Plot with Linear Regression
Library: pygal 3.1.0 | Python 3.13.13
Quality: 82/100 | Updated: 2026-05-06
"""

import os

import numpy as np
import pygal
from pygal.style import Style
from scipy import stats


# Theme tokens
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")

# Data - Study hours vs exam scores
np.random.seed(42)
n_points = 80
x = np.random.uniform(1, 10, n_points)
y = 45 + 5 * x + np.random.normal(0, 8, n_points)

# Calculate linear regression
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
r_squared = r_value**2

# Generate regression line points
x_line = np.linspace(min(x), max(x), 100)
y_line = slope * x_line + intercept

# Calculate confidence interval (95%)
n = len(x)
x_mean = np.mean(x)
se_y = np.sqrt(np.sum((y - (slope * x + intercept)) ** 2) / (n - 2))
t_val = stats.t.ppf(0.975, n - 2)
se_line = se_y * np.sqrt(1 / n + (x_line - x_mean) ** 2 / np.sum((x - x_mean) ** 2))
ci_upper = y_line + t_val * se_line
ci_lower = y_line - t_val * se_line

# Custom style for 4800x2700 px canvas
custom_style = Style(
    background=PAGE_BG,
    plot_background=PAGE_BG,
    foreground=INK,
    foreground_strong=INK,
    foreground_subtle=INK_MUTED,
    colors=IMPRINT,
    title_font_size=28,
    label_font_size=22,
    major_label_font_size=18,
    legend_font_size=16,
    value_font_size=14,
    stroke_width=3,
)

# Create XY scatter chart
chart = pygal.XY(
    width=4800,
    height=2700,
    style=custom_style,
    title="scatter-regression-linear · pygal · anyplot.ai",
    x_title="Study Hours (hrs)",
    y_title="Exam Score (points)",
    show_legend=True,
    legend_at_bottom=False,
    legend_box_size=24,
    dots_size=16,
    stroke=False,
    show_x_guides=True,
    show_y_guides=True,
    truncate_legend=-1,
)

# Add scatter points
scatter_data = [{"value": (float(x[i]), float(y[i]))} for i in range(n_points)]
chart.add("Data Points", scatter_data, dots_size=16, stroke=False)

# Add confidence interval band (95%) as semi-transparent fill
ci_band_data = []
step = 3
for i in range(0, len(x_line), step):
    ci_band_data.append((float(x_line[i]), float(ci_upper[i])))
for i in range(len(x_line) - 1, -1, -step):
    ci_band_data.append((float(x_line[i]), float(ci_lower[i])))
ci_band_data.append(ci_band_data[0])

chart.add(
    "95% CI Band", ci_band_data, stroke=True, fill=True, show_dots=False, stroke_style={"width": 1, "opacity": 0.2}
)

# Add regression line (thicker, more prominent)
equation = f"y = {slope:.2f}x + {intercept:.1f}, R² = {r_squared:.3f}"
chart.add(
    "Regression Line",
    [(float(x_line[i]), float(y_line[i])) for i in range(len(x_line))],
    stroke=True,
    show_dots=False,
    stroke_style={"width": 5},
)

# Render to PNG and HTML
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
    f.write(chart.render())

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

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