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Scatter Plot Generator Examples
The Scatter Plot Generator creates scatter plots and bubble charts for visualizing relationships between variables. Below are examples of different chart types and configurations.
Basic Scatter Plot — Correlation
Visualize the relationship between study hours and test scores:
Data (hours studied vs. test score):
Hours Score
1 45
2 52
3 61
4 68
5 74
6 79
7 85
8 88
9 91
10 95
Chart settings:
X-axis: Hours Studied
Y-axis: Test Score (%)
Trend line: Linear regression
Result: Strong positive correlation
Correlation coefficient (r): 0.99
Trend line: y = 5.8x + 38.2
R²: 0.98 (98% of variance explained)
Negative Correlation Example
Relationship between price and units sold:
Data (price vs. units sold):
Price ($) Units Sold
10 980
15 820
20 650
25 510
30 390
35 280
40 190
45 120
50 80
Correlation coefficient (r): -0.99
Interpretation: Strong negative correlation —
as price increases, units sold decreases.
No Correlation Example
Shoe size vs. IQ score (no relationship):
Data:
Shoe Size IQ
8 105
9 98
10 112
11 95
9 118
10 103
8 97
11 108
10 115
9 101
Correlation coefficient (r): 0.03
Interpretation: No meaningful correlation.
Points are scattered randomly with no trend.
Multiple Data Series
Compare two groups on the same chart:
Series 1 — Group A (blue circles):
X: 2, 4, 6, 8, 10
Y: 15, 28, 42, 55, 68
Series 2 — Group B (red squares):
X: 2, 4, 6, 8, 10
Y: 8, 14, 22, 31, 40
Chart shows both groups with separate trend lines:
Group A: steeper slope (faster growth)
Group B: shallower slope (slower growth)
Legend identifies each series
Bubble Chart — Three Variables
Visualize country GDP, life expectancy, and population:
Country GDP/capita ($) Life Exp (yrs) Population (M)
United States 65,000 79 331
Germany 46,000 81 83
Japan 40,000 84 126
Brazil 8,700 76 213
India 2,100 70 1,380
Nigeria 2,000 55 206
Chart settings:
X-axis: GDP per capita
Y-axis: Life expectancy
Bubble size: Population (larger = more people)
Observation: Higher GDP generally correlates with
longer life expectancy. India and China are large
bubbles in the lower-left quadrant.
Outlier Detection
Identify unusual data points:
Dataset: Employee salary vs. years of experience
Most points follow the trend:
5 years → $75,000
10 years → $95,000
15 years → $115,000
Outliers detected (marked in red):
Point A: 3 years, $140,000 (unusually high — possible data error or executive hire)
Point B: 20 years, $55,000 (unusually low — possible part-time or different role)
Statistical method: Points beyond 2 standard deviations from the trend line
Logarithmic Scale
Use log scale for data spanning multiple orders of magnitude:
Data: Website traffic vs. revenue (log scale)
Linear scale: Small sites are compressed near zero,
making patterns hard to see.
Log scale: All data points are visible and spread out.
10 visitors → $5 revenue
100 visitors → $50 revenue
1,000 visitors → $500 revenue
10,000 visitors → $5,000 revenue
100,000 visitors → $50,000 revenue
Log scale reveals the proportional relationship clearly.
Trend Line Options
Different regression types for different data patterns:
Linear regression:
y = mx + b
Best for: data with a straight-line trend
Example: height vs. weight
Polynomial (quadratic):
y = ax² + bx + c
Best for: data with a curved trend
Example: projectile motion, diminishing returns
Exponential:
y = ae^(bx)
Best for: data with exponential growth/decay
Example: compound interest, population growth
Power:
y = ax^b
Best for: data following a power law
Example: city size vs. rank (Zipf's law)
Color-Coded Categories
Encode a categorical variable through point colors:
Data: Sales performance by region
Points colored by region:
🔵 North: (Q1: $120K, Q2: $145K, Q3: $138K, Q4: $162K)
🔴 South: (Q1: $95K, Q2: $108K, Q3: $125K, Q4: $140K)
🟢 East: (Q1: $180K, Q2: $195K, Q3: $188K, Q4: $210K)
🟡 West: (Q1: $75K, Q2: $82K, Q3: $91K, Q4: $105K)
X-axis: Quarter
Y-axis: Revenue ($K)
Color legend identifies each region
Export Options
PNG: High-resolution image for reports and presentations
SVG: Vector format for scalable print quality
HTML: Interactive chart — hover over points to see exact values
CSV: Export the underlying data with calculated statistics
- Enter data in a table editor or paste CSV/TSV from a spreadsheet
- Multiple data series with different colors and symbols
- Linear, polynomial, exponential, and power trend lines
- Correlation coefficient and R² value displayed
- Bubble chart mode for three-variable visualization
- Outlier detection with statistical highlighting
- Logarithmic scale for wide-range data
- Export as PNG, SVG, or interactive HTML