# Linear Interpolation Calculator

### Introduction

Welcome to our in-depth look at interpolation, a fundamental concept in mathematics and data analysis. Interpolation, often contrasted with extrapolation, is a technique used to estimate unknown values by utilizing a set of known data points. In this guide, we’ll explore what interpolation is, its key formulas, how it differs from extrapolation, and answer some frequently asked questions.

### What is Interpolation?

Definition: Interpolation is a method of constructing new data points within the range of a discrete set of known data points. It’s a prediction technique, based on the assumption that the data can be fitted into a model to estimate intermediate values.

Importance in Various Fields: Interpolation is pivotal in fields like science, where it helps in predicting unmeasured values, and in digital imaging, where it enhances image resolution. It’s also crucial in engineering for designing and testing models.

Types of Interpolation: The most common types are linear, where data points are connected via straight lines; polynomial, involving a polynomial function; and spline interpolation, which uses low-degree polynomials in each of the intervals.

### Interpolation Formula

General Formula: The interpolation formula is a mathematical expression used to find new data points. The formula’s complexity varies with the interpolation type.

Linear Interpolation Formula
y = y₁ + $$\frac{(x – x₁)}{(x₂ – x₁)} * (y₂ – y₁)$$
• x – The point for which we want to interpolate
• x₁, y₁ – The coordinates of the first known data point
• x₂, y₂ – The coordinates of the second known data point
• y – The interpolated value at point x

### Interpolation vs Extrapolation

Definitions and Differences: While interpolation predicts within the range of existing data, extrapolation ventures outside this range. It’s like filling gaps versus predicting future trends.

When to Use Each: Interpolation is used when accuracy is crucial within a known range. Extrapolation is applied for forecasting and trend analysis but comes with higher uncertainty.

Examples: Using interpolation to predict a missing temperature reading for a specific day, versus using extrapolation to predict the temperature for the next month.

Q1: What are common mistakes in using interpolation?

Overestimating its accuracy over large ranges and choosing the wrong type for your data.

Q2: Can interpolation predict future trends?

No, that’s the realm of extrapolation. Interpolation works within known data points.

Q3: How does interpolation work in computer graphics?

It’s used to render high-resolution images from lower-resolution data by estimating intermediate pixel values.

Q4: What is the difference between interpolation and regression?

While interpolation finds values within a dataset, regression is about finding a relationship between variables.

### Conclusion

Understanding interpolation is essential in data analysis, offering a window into trends and patterns hidden within data points. While it’s a powerful tool, knowing when and how to use it is key to extracting its full potential.