Interpolation Formula: Interpolation is a mechanism for discovering new values for any function based on a set of values. This formula is used to determine an unknown value on a point. If the linear interpolation formula is to be employed, the two specified points should be used to find a new value. The “n” set of numbers should be provided when compared to Lagrange’s interpolation formula, and Lagrange’s method should be utilized to get the new value.

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The technique of determining a value between two points on a line or curve is known as interpolation. To recall what it implies, consider the initial part of the term, ‘inter,’ to imply ‘enter,’ which reminds us to examine ‘within’ the data we started with. This tool, interpolation, is useful not only in statistics, but also in science, business, and any other situation where values that fall between two existent data points must be predicted.


Formula for Linear Interpolation


Formula for Linear Interpolation


The linear interpolant is the straight line between the two known locations, which are given by the coordinates displaystyle (x 0,y 0) and displaystyle (x 1,y 1). The value y along the straight line is provided by the equation of slopes for a value x in the interval displaystyle (x 0,x 1).


frac y-y 0x-x 0=frac y 1-y 0x 1-x 0, frac y 1-y 0x 1-x 0, frac y 1-y 0x 1-x 0, frac y 1-y 0x 1-x 0, frac y 1-y 0x 1-x 0, frac y 1-y 0


This is mathematically deduced from the illustration on the right. With n = 1, it’s a specific instance of polynomial interpolation.


When you solve this equation for y, the unknown value at x, you get


y=y 0+(x-x 0) y=y 0+(x-x 0) y=y 0+(x-x 0) y=y 0+(x-x


frac y 1-y 0x 1-x 0=frac y 0(x 1-x)+y 1(x-x 0)x 1-x 0, frac y 0(x 1-x)+y 1(x-x 0)x 1-x 0,


which is the linear interpolation formula in the interval displaystyle (x 0,x 1) Within this range, the formula is the same as linear extrapolation.


This formula is also known as a weighted average formula. The weights are inversely proportional to the distance between the end points and the unknown point; the closer the point, the greater the influence. The weights are textstyle frac x-x 0x 1-x 0 and textstyle frac x 1-xx 1-x 0, which are normalized distances between the unknown point and each of the end points, respectively. Because they add up to one,


y=y 0left y=y 0left y=y 0left y=y 0left y=y 0left


(x-x 0x 1-x 0right) (1-frac x-x 0x 1-x 0right)


+y {1}\left


(x 1-xx 1-x 0right) (1-frac x 1-xx 1-x 0right)


=y {0}\left


(x-x 0x 1-x 0right) (1-frac x-x 0x 1-x 0right)


+y 1left(frac x-x 0x 1-x 0right), +y 1right(frac x-x 0x 1-x 0right), +y 1right(frac x-x 0x 1-x 0right), +y 1right(frac


Calculator for Interpolation Formulas


Examples that have been solved


Question 1: Given a set of values (2, 6), (5, 9) and the interpolation formula, what is the value of y at x = 8?




x0=8,x1=2,x2=5,y1=6,y2=9y=y1+ are the known values.








6 + 6 Equals y


12 = y


What is the linear interpolation method, and how does it work?


The simplest method of obtaining values at positions between data points is linear interpolation. Straight line segments are used to connect the points.


What is the procedure for calculating the interpolation between two numbers?


Understand the formula for linear interpolation. y = y1 + ((x – x1) / (x2 – x1)) * (y2 – y1), where x is the known value, y is the unknown value, x1 and y1 are coordinates below the known x value, and x2 and y2 are positions above the x value.


What is the method of interpolation?


Interpolation is a method of producing new data points within the range of a discrete set of known data points in the mathematical discipline of numerical analysis. A few data points from the original function can be interpolated to create a more simplified function that is still quite close to the original.


Excel Formula for Interpolation


Excel Formula for Interpolation


Here’s an example of how to use interpolation to demonstrate the concept. Every other day, a gardener measured and tracked the growth of a tomato plant she had planted. This gardener is inquisitive, so she wants to know how tall her plant was on the fourth day.


Also see: Average and Instantaneous Change Rates


This is what her table of observations looked like:




It’s not difficult to deduce from the chart that the plant was probably 6 mm tall on the fourth day. This is due to the fact that this well-behaved tomato plant grew in a straight line, with a linear relationship between the number of days measured and the plant’s height growth. The points formed a straight line in the linear pattern. We could even make an educated guess by graphing the data.


But what if the plant didn’t grow in a predictable linear pattern? What if its development like this?


What would the gardener do if he needed to make a guess based on the above curve? That’s where the interpolation formula can help.


Thermodynamic Interpolation Formula


Linear interpolation has been used to fill gaps in tables since antiquity. Consider the following scenario: you have a table with the population of a country in 1970, 1980, 1990, and 2000, and you want to estimate the population in 1994. Linear interpolation is a simple method for accomplishing this. According to legend, Babylonian astronomers and mathematicians in Seleucid Mesopotamia (last three centuries BC) and Hipparchus, a Greek astronomer and mathematician, used linear interpolation for tabulation (2nd century BC). Ptolemy’s Almagest (2nd century AD) contains a description of linear interpolation.


In computer graphics, the basic operation of linear interpolation between two variables is extensively utilized. It is sometimes referred to as a lerp in that field’s jargon. For the operation, the term can be used as a verb or a noun. “Bresenham’s algorithm incrementally lerps between the two endpoints of the line,” for example.


All modern computer graphics processors include Lerp operations in their hardware. They’re frequently used as building blocks for more complex operations: bilinear interpolation, for example, can be done in three lerps. It’s also a fantastic approach to construct accurate lookup tables with a speedy lookup for smooth functions without having too many table entries because this operation is cheap.