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.
""" anyplot.ai
scatter-regression-linear: Scatter Plot with Linear Regression
Library: plotly 6.9.0 | Python 3.13.14
Quality: 90/100 | Updated: 2026-08-05
"""
import os
import numpy as np
import plotly.graph_objects as go
# Theme tokens (Imprint palette — prompts/default-style-guide.md "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"
GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
FONT_FAMILY = "Arial, Helvetica, sans-serif"
BRAND = "#009E73" # Imprint palette position 1 — ALWAYS first series
ACCENT = "#C475FD" # Imprint palette position 2
# Data - study hours vs exam scores (clipped to a realistic 0-100% range)
np.random.seed(42)
n_points = 100
study_hours = np.random.uniform(2, 10, n_points)
noise = np.random.normal(0, 6, n_points)
exam_scores = np.clip(study_hours * 4.5 + 45 + noise, 0, 100)
# Linear regression
n = len(study_hours)
x_mean = np.mean(study_hours)
slope, intercept = np.polyfit(study_hours, exam_scores, 1)
y_pred = slope * study_hours + intercept
residuals = exam_scores - y_pred
ss_res = np.sum(residuals**2)
ss_tot = np.sum((exam_scores - np.mean(exam_scores)) ** 2)
r_squared = 1 - ss_res / ss_tot
# Regression line and confidence interval
x_line = np.linspace(study_hours.min() - 0.5, study_hours.max() + 0.5, 100)
y_line = slope * x_line + intercept
# Calculate 95% confidence interval
ss_xx = np.sum((study_hours - x_mean) ** 2)
mse = ss_res / (n - 2)
se_line = np.sqrt(mse * (1 / n + (x_line - x_mean) ** 2 / ss_xx))
t_val = 1.98
ci_upper = y_line + t_val * se_line
ci_lower = y_line - t_val * se_line
# Create figure
fig = go.Figure()
# Confidence interval band
fig.add_trace(
go.Scatter(
x=np.concatenate([x_line, x_line[::-1]]),
y=np.concatenate([ci_upper, ci_lower[::-1]]),
fill="toself",
fillcolor="rgba(0, 158, 115, 0.15)",
line=dict(color="rgba(0, 158, 115, 0.35)", width=1),
hoverinfo="skip",
name="95% CI",
showlegend=True,
)
)
# Scatter points
fig.add_trace(
go.Scatter(
x=study_hours,
y=exam_scores,
mode="markers",
marker=dict(size=8, color=BRAND, opacity=0.55, line=dict(width=0.5, color=PAGE_BG)),
name="Data points",
hovertemplate="Study Hours: %{x:.1f}<br>Exam Score: %{y:.1f}<extra></extra>",
)
)
# Regression line
fig.add_trace(
go.Scatter(
x=x_line,
y=y_line,
mode="lines",
line=dict(color=ACCENT, width=3),
name=f"Linear Regression (R² = {r_squared:.3f})",
hoverinfo="skip",
)
)
# Equation annotation
equation = f"y = {slope:.2f}x + {intercept:.1f}"
fig.add_annotation(
x=0.98,
y=0.05,
xref="paper",
yref="paper",
text=f"{equation}<br>R² = {r_squared:.3f}",
showarrow=False,
font=dict(size=12, color=INK),
align="right",
bgcolor=ELEVATED_BG,
borderpad=8,
)
# Layout
title_text = "scatter-regression-linear · python · plotly · anyplot.ai"
fig.update_layout(
autosize=False,
font=dict(family=FONT_FAMILY, color=INK),
title=dict(text=title_text, font=dict(size=16, color=INK), x=0.5, xanchor="center"),
xaxis=dict(
title=dict(text="Study Hours per Day", font=dict(size=12, color=INK)),
tickfont=dict(size=10, color=INK_SOFT),
gridcolor=GRID,
showgrid=True,
zeroline=False,
linecolor=INK_SOFT,
linewidth=1,
showspikes=True,
spikemode="across",
spikesnap="cursor",
spikedash="dot",
spikecolor=INK_SOFT,
spikethickness=1,
),
yaxis=dict(
title=dict(text="Exam Score (%)", font=dict(size=12, color=INK)),
tickfont=dict(size=10, color=INK_SOFT),
gridcolor=GRID,
showgrid=True,
zeroline=False,
linecolor=INK_SOFT,
linewidth=1,
showspikes=True,
spikemode="across",
spikesnap="cursor",
spikedash="dot",
spikecolor=INK_SOFT,
spikethickness=1,
),
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
legend=dict(x=0.02, y=0.98, xanchor="left", yanchor="top", font=dict(size=10, color=INK_SOFT), bgcolor=ELEVATED_BG),
margin=dict(l=80, r=40, t=80, b=60),
hovermode="closest",
)
# Save as PNG and HTML — hard target 3200x1800 (see prompts/library/plotly.md "Canvas")
fig.write_image(f"plot-{THEME}.png", width=800, height=450, scale=4)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/scatter-regression-linear/plotly/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "scatter-regression-linear",
"language": "python",
"library": "plotly",
"page": "https://anyplot.ai/scatter-regression-linear/python/plotly",
"hub": "https://anyplot.ai/scatter-regression-linear",
"code_json": "https://api.anyplot.ai/specs/scatter-regression-linear/plotly/code",
"spec_json": "https://api.anyplot.ai/specs/scatter-regression-linear",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-regression-linear/python/plotly/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-regression-linear/python/plotly/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/scatter-regression-linear/python/plotly/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/scatter-regression-linear/python/plotly/plot-dark.html",
"quality_score": 90.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Scatter Plot with Linear Regression on anyplot.ai.