# Interpolation Calculator: Definition, Formula, Examples, and FAQ

Published: 4/26/2021 Last updated: 2/25/2023

Interpolation is a mathematical technique used to estimate values between two known data points. It involves finding a function that passes through a set of given data points and using it to predict the value of a variable at an intermediate point. This process is useful when we have incomplete or missing data and want to estimate the value of a variable at a specific point. In this article, we will explore the concept of interpolation, the formula used to calculate it, and provide examples to help you understand it better.

## Definition of Interpolation

Interpolation is the process of finding an estimate of the value of a variable at an intermediate point between two known data points. It involves finding a function that passes through a set of given data points and using it to predict the value of a variable at a specific point. Interpolation is commonly used in engineering, physics, and other sciences to estimate values that are not directly measured or to fill in missing data points in a dataset.

## Formula for Interpolation

The formula for interpolation is based on the concept of a linear equation. A linear equation is an equation of the form y = mx + b, where m is the slope of the line and b is the y-intercept. The slope of the line is the ratio of the change in y to the change in x between two points on the line.

The formula for interpolation is:

y = y1 + ((x - x1) / (x2 - x1)) * (y2 - y1)

Where:

• y1 and y2 are the values of the dependent variable at the two known data points
• x1 and x2 are the values of the independent variable at the two known data points
• x is the value of the independent variable at the point where we want to estimate the value of the dependent variable
• y is the estimated value of the dependent variable at the point where we want to estimate the value of the dependent variable

## Examples of Interpolation

Let's consider an example to help illustrate the concept of interpolation. Suppose we have the following data:

x y
2 4
4 8

Using this data, let's estimate the value of y at x=3.

Using the formula for interpolation, we get:

y = 4 + ((3 - 2) / (4 - 2)) * (8 - 4) = 6

Therefore, the estimated value of y at x=3 is 6.

## Explanation of Interpolation

Interpolation is a useful technique for estimating the value of a variable at an intermediate point between two known data points. It involves finding a function that passes through a set of given data points and using it to predict the value of a variable at a specific point. The formula for interpolation is based on the concept of a linear equation, and it involves calculating the slope of a line that passes through the two known data points and using it to estimate the value of the dependent variable at the desired point.

#### What is the difference between interpolation and extrapolation?

The main difference between interpolation and extrapolation is that interpolation is used to estimate the value of a variable at an intermediate point between two known data points, whereas extrapolation is used to estimate the value of a variable beyond the range of the known data points. In other words, interpolation is used to estimate values within the range of the data, whereas extrapolation is used to estimate values outside the range of the data.

#### What are some common applications of interpolation?

Interpolation is commonly used in engineering, physics, and other sciences to estimate values that are not directly measured or to fill in missing data points in a dataset. It is also used in computer graphics to create smooth curves and surfaces, and in financial modeling to estimate the value of assets or securities.

#### What are the limitations of interpolation?

One limitation of interpolation is that it assumes a linear relationship between the independent and dependent variables. In reality, the relationship between the variables may be non-linear, which can lead to errors in the estimation process. Another limitation is that it is sensitive to outliers in the data, which can skew the estimated values. Finally, interpolation is only as accurate as the data that it is based on, so it is important to use high-quality data to get accurate estimates.

## Conclusion

Interpolation is a useful technique for estimating the value of a variable at an intermediate point between two known data points. It involves finding a function that passes through a set of given data points and using it to predict the value of a variable at a specific point. The formula for interpolation is based on the concept of a linear equation, and it involves calculating the slope of a line that passes through the two known data points and using it to estimate the value of the dependent variable at the desired point.

Interpolation is commonly used in engineering, physics, and other sciences to estimate values that are not directly measured or to fill in missing data points in a dataset. It is also used in computer graphics and financial modeling. However, interpolation has its limitations, including the assumption of a linear relationship between the variables and sensitivity to outliers in the data.

## Get Started with Interpolation Calculator

Now that you understand what interpolation is and how it works, you can start using an interpolation calculator to estimate values for your own datasets. There are many free online calculators available that can perform interpolation calculations for you. Some popular options include:

Simply input your data points and the value that you want to estimate, and the calculator will use the formula for linear interpolation to provide you with an estimated value. Keep in mind that the accuracy of the estimate will depend on the quality of the data that you provide and the assumptions made by the calculator.

## Final Thoughts

Interpolation is a powerful tool that can be used to estimate the value of a variable at an intermediate point between two known data points. By finding a function that passes through a set of given data points, we can use it to predict the value of a variable at a specific point. Interpolation is used in many different fields, including engineering, physics, computer graphics, and finance.

While interpolation is a useful technique, it has its limitations. It assumes a linear relationship between the independent and dependent variables, and is sensitive to outliers in the data. However, by understanding these limitations and using high-quality data, we can get accurate estimates and make informed decisions based on our data.

So the next time you need to estimate a value between two data points, give interpolation a try. With the right tools and knowledge, it can be a powerful tool in your data analysis toolbox.                                                                              