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: matplotlib 3.10.9 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-06
"""
import os
import matplotlib.pyplot as plt
import numpy as np
# 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" # Okabe-Ito position 1
SECONDARY = "#C475FD" # Okabe-Ito position 2 for regression line
# Data: Study hours vs exam scores (realistic educational context)
np.random.seed(42)
n_points = 80
x = np.random.uniform(1, 10, n_points) # Study hours
noise = np.random.normal(0, 8, n_points)
y = 35 + 6 * x + noise # Exam scores
y = np.clip(y, 20, 100) # Realistic score range
# Linear regression using numpy polyfit (leveraging library ecosystem)
coefficients = np.polyfit(x, y, 1)
slope, intercept = coefficients[0], coefficients[1]
# Calculate R-squared
y_pred = np.polyval(coefficients, x)
ss_res = np.sum((y - y_pred) ** 2)
ss_tot = np.sum((y - np.mean(y)) ** 2)
r_squared = 1 - (ss_res / ss_tot)
# Regression line and 95% confidence interval
x_line = np.linspace(x.min() - 0.5, x.max() + 0.5, 100)
y_line = np.polyval(coefficients, x_line)
x_mean = np.mean(x)
ss_xx = np.sum((x - x_mean) ** 2)
se_y = np.sqrt(ss_res / (n_points - 2))
se_line = se_y * np.sqrt(1 / n_points + (x_line - x_mean) ** 2 / ss_xx)
t_val = 1.99 # 95% CI
ci_upper = y_line + t_val * se_line
ci_lower = y_line - t_val * se_line
# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Confidence interval band (using SECONDARY color with reduced alpha)
ax.fill_between(x_line, ci_lower, ci_upper, alpha=0.2, color=SECONDARY, label="95% CI")
# Scatter points (BRAND green as first series)
ax.scatter(x, y, s=200, alpha=0.7, color=BRAND, edgecolors=PAGE_BG, linewidth=0.5, zorder=3)
# Regression line (using SECONDARY color)
ax.plot(x_line, y_line, color=SECONDARY, linewidth=3, label="Regression Line", zorder=2)
# Annotations with theme-adaptive styling
equation = f"y = {slope:.2f}x + {intercept:.2f}"
r_text = f"R² = {r_squared:.3f}"
ax.text(
0.05,
0.95,
f"{equation}\n{r_text}",
transform=ax.transAxes,
fontsize=18,
verticalalignment="top",
color=INK,
bbox={"boxstyle": "round,pad=0.4", "facecolor": ELEVATED_BG, "edgecolor": INK_SOFT, "alpha": 0.95},
)
# Styling with theme-adaptive chrome
ax.set_xlabel("Study Hours (hrs)", fontsize=20, color=INK)
ax.set_ylabel("Exam Score (points)", fontsize=20, color=INK)
ax.set_title("scatter-regression-linear · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
# Grid styling (subtle, y-axis preferred)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)
# Spine styling
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for spine in ("left", "bottom"):
ax.spines[spine].set_color(INK_SOFT)
# Legend styling
leg = ax.legend(fontsize=16, loc="lower right")
if leg:
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
leg.get_frame().set_linewidth(0.8)
for text in leg.get_texts():
text.set_color(INK_SOFT)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Scatter Plot with Linear Regression on anyplot.ai.