Scatter Diagram Generator
Analyze Relationships & Correlations Between Variables
Scatter Diagram Generator
Temperature vs Pressure Relationship
Legend
Correlation Analysis
Correlation (r): 0.9828
R-squared: 0.9659
Strength: Very Strong
Type: Positive
Linear Regression
Slope: 0.6945
Intercept: 13.7273
Equation: y = 0.69x + 13.73
Statistical Significance
Sample Size: 10
Significance: Not significant
Variance Explained: 96.6%
📊 Correlation Interpretation
Correlation Strength: Very strong relationship between variables
Direction: Positive correlation - as X increases, Y tends to increase
Practical Application: Strong predictive model - suitable for forecasting
📊 About Scatter Diagrams
Scatter diagrams (scatter plots) are graphical tools used to investigate the relationship between two variables and identify potential correlations.
Key Features
- • X-axis: Independent variable
- • Y-axis: Dependent variable
- • Each point represents one data pair
- • Pattern reveals relationship strength
Applications
- • Process optimization
- • Root cause analysis
- • Predictive modeling
- • Quality improvement
🔗 Correlation Types
Positive Correlation
As X increases, Y increases. Points form an upward trend.
Example: Temperature vs. Ice cream sales
Negative Correlation
As X increases, Y decreases. Points form a downward trend.
Example: Price vs. Demand
No Correlation
No apparent relationship. Points are randomly scattered.
Example: Shoe size vs. Intelligence
🏭 Practical Example
Scenario: Manufacturing Process Optimization
A chemical plant wants to optimize reaction temperature to maximize product yield:
Data Collection:
- • X-variable: Reaction temperature (°C)
- • Y-variable: Product yield (%)
- • Sample size: 30 production batches
- • Time period: 3 months of operation
Analysis Results:
- • Strong positive correlation (r = 0.85)
- • Optimal temperature range: 180-190°C
- • Expected yield increase: 8-12%
- • Action: Standardize temperature control
📏 Correlation Strength Guide
Correlation Coefficient (r)
Interpretation Tips
- • Causation ≠ Correlation: Strong correlation doesn't prove causation
- • Outliers: Single extreme points can skew results
- • Sample Size: Larger samples give more reliable results
- • Non-linear: Some relationships aren't straight lines
- • Context: Always consider the practical significance
⚠️ Common Pitfalls
Spurious Correlation
Two variables may correlate due to coincidence or a hidden third variable. Always verify with domain knowledge.
Range Restriction
Limited data ranges can hide or artificially reduce correlations. Collect data across full operational ranges.
Confounding Variables
External factors may influence both variables. Control or account for these in your analysis.