Interpolation Methods
Understanding different interpolation methods helps you choose the right approach for your data. Each method has its own characteristics, advantages, and ideal use cases.
Available Methods
Linear Interpolation
The simplest method, connecting data points with straight lines.
Best for:
- Quick calculations with minimal overhead
- Data that naturally has sharp transitions
- When computational speed is critical
Polynomial Interpolation
Uses polynomial functions to pass through all data points.
Best for:
- Small datasets with smooth underlying trends
- Mathematical modeling
- When exact point matching is required
Spline Interpolation
Creates smooth curves using piecewise polynomials.
Best for:
- Large datasets requiring smooth curves
- Visualization and graphics
- General-purpose interpolation with good performance
Choosing the Right Method
Not sure which method to use? Our comprehensive comparison guide breaks down the characteristics, performance, and ideal applications of each method.
Method Characteristics Summary
| Method | Smoothness | Speed | Complexity | Best Use Case |
|---|---|---|---|---|
| Linear | Sharp corners | Very Fast | Simple | Quick calculations, piecewise data |
| Polynomial | Very smooth | Moderate | Complex | Small datasets, mathematical modeling |
| Spline | Smooth | Fast | Moderate | General purpose, large datasets |
Next Steps
- Read the detailed Method Comparison guide
- See these methods in action with our Excel Add-in
- Explore real-world Use Cases