Scatter Plot with Linear Regression — lets-plot

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 lets-plot

Python source (lets-plot)

""" anyplot.ai
scatter-regression-linear: Scatter Plot with Linear Regression
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 88/100 | Updated: 2026-05-06
"""

import os

import numpy as np
import pandas as pd
from lets_plot import *
from lets_plot.export import ggsave as export_ggsave


LetsPlot.setup_html()

# 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"
BRAND = "#009E73"
SECONDARY = "#C475FD"

# Data - Advertising spend vs sales revenue relationship
np.random.seed(42)
n = 100
advertising_spend = np.random.uniform(10, 100, n)
sales_revenue = 2.5 * advertising_spend + 50 + np.random.randn(n) * 25

df = pd.DataFrame({"advertising_spend": advertising_spend, "sales_revenue": sales_revenue})

# Calculate regression statistics
x_mean = np.mean(advertising_spend)
y_mean = np.mean(sales_revenue)
slope = np.sum((advertising_spend - x_mean) * (sales_revenue - y_mean)) / np.sum((advertising_spend - x_mean) ** 2)
intercept = y_mean - slope * x_mean

# Calculate R²
y_pred = slope * advertising_spend + intercept
ss_res = np.sum((sales_revenue - y_pred) ** 2)
ss_tot = np.sum((sales_revenue - y_mean) ** 2)
r_squared = 1 - (ss_res / ss_tot)

# Create annotation text
equation_text = f"y = {slope:.2f}x + {intercept:.1f}"
r2_text = f"R² = {r_squared:.3f}"
annotation_text = f"{equation_text}\n{r2_text}"

# Position for annotation
annotation_x = 15
annotation_y = sales_revenue.max() - 10

# Plot
plot = (
    ggplot(df, aes(x="advertising_spend", y="sales_revenue"))
    + geom_point(
        color=BRAND,
        size=4,
        alpha=0.65,
        tooltips=layer_tooltips()
        .line("Ad Spend|$@advertising_spend{.1f}K")
        .line("Sales|$@sales_revenue{.1f}K"),
    )
    + geom_smooth(
        method="lm", color=SECONDARY, size=2, se=True, level=0.95, fill=SECONDARY, fill_alpha=0.15
    )
    + geom_text(
        x=annotation_x, y=annotation_y, label=annotation_text, size=14, color=INK, hjust=0, vjust=1, family="sans-serif"
    )
    + labs(
        x="Advertising Spend ($K)", y="Sales Revenue ($K)", title="scatter-regression-linear · letsplot · anyplot.ai"
    )
    + ggsize(1600, 900)
    + theme_minimal()
    + theme(
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG),
        panel_grid_major=element_line(color=INK_SOFT, size=0.3, linetype="blank"),
        axis_text=element_text(size=16, color=INK_SOFT),
        axis_title=element_text(size=20, color=INK),
        plot_title=element_text(size=24, color=INK),
        axis_line=element_line(color=INK_SOFT, size=0.5),
    )
)

# Save outputs
export_ggsave(plot, filename=f"plot-{THEME}.png", path=".", scale=3)
export_ggsave(plot, filename=f"plot-{THEME}.html", path=".")

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

Scatter Plot with Linear Regression — lets-plot

Python source (lets-plot)

Other implementations