How to Interpolate Missing Values in Excel?
Last Updated :
14 Jul, 2022
Linear Interpolation means estimating a missing value by connecting dots in the straight line in increasing order. It estimates the unknown value in the same increasing order as the previous values. The default method used by Interpolation is Linear so while applying it one does not need to specify it. Frequently one might have at least one missing quality in a series in Excel that you might want to fill in. The most straightforward method for filling in missing qualities is to utilize the Fill Series capability inside the Editing area on the Home tab.
This instructional exercise gives two instances of how to involve this capability practically speaking.
Fill in Missing Values for a Linear Trend
Assume we have the accompanying dataset with a couple of missing qualities in Excel,
On the off chance that we make a speedy line graph of this information, we'll see that the information seems to pursue a straight direction:
To fill in the missing qualities, we can feature the reach beginning when the missing qualities, then click Home > Editing > Fill > Series.
On the off chance that we leave the Type as Linear, Excel will utilize the accompanying equation to figure out what step is worth using to fill in the missing information:
Step = (End - Start)/(#Missing obs + 1)
For this model, it decides the step worth to be: (34 - 21)/(4 + 1) = 2.6. When we click OK, Excel consequently fills in the missing quantities by adding 2.6 to each resulting esteem:
Fill in Missing Values for a Growth Trend
Assume we have the accompanying dataset with a couple of missing qualities in Excel,
In the event that we make a fast-line outline of this information, we'll see that the information seems to follow an outstanding (or "development") pattern:
To fill in the missing qualities, we can feature the reach beginning when the missing qualities, then, at that point, click Home > Editing > Fill > Series.
On the off chance that we select the Type as Growth and snap the case close to Trend, Excel consequently recognizes the development pattern in the information and fills in the missing values. When we click OK, Excel fills in the missing qualities:
From the plot, we can see that the filled-in values match the general pattern of the information very well.
Similar Reads
How to find missing values in a list in R Missing values are frequently encountered in data analysis. In R Programming Language effectively dealing with missing data is critical for correct analysis and interpretation. Whether you're a seasoned data scientist or a new R user, understanding how to identify missing values is critical. In this
3 min read
How to find missing values in a matrix in R In this article, we will examine various methods for finding missing values in a matrix by using R Programming Language. What are missing values?The data points in a dataset that are missing for a particular variable are known as missing values. These missing values are represented in various ways s
3 min read
How to Impute Missing Values in R? In this article, we will discuss how to impute missing values in R programming language. In most datasets, there might be missing values either because it wasn't entered or due to some error. Replacing these missing values with another value is known as Data Imputation. There are several ways of imp
3 min read
How to find missing values in a factor in R Missing values are a regular occurrence in data analysis, and they might limit the precision and trustworthiness of your findings. When working with factors in R, the process gets considerably more complex. Have no fear! This article is your guide through the maze of missing values in R factors. We'
2 min read
How to Remove Spaces in Excel Have you ever wondered you keep searching data on Excel but it is not returning any values and many times you are trying to add columns or data but you are unable to do so? Well, these are some common problems we all have faced in our life. But do you know why it happens? This happens because of ext
8 min read
How to deal with missing values in a Timeseries in Python? It is common to come across missing values when working with real-world data. Time series data is different from traditional machine learning datasets because it is collected under varying conditions over time. As a result, different mechanisms can be responsible for missing records at different tim
9 min read
Handling Missing Values in Time Series Data Handling missing values in time series data in R is a crucial step in the data preprocessing phase. Time series data often contains gaps or missing observations due to various reasons such as sensor malfunctions, human errors, or other external factors. In R Programming Language dealing with missing
5 min read
Insert Rows for Missing Dates in R DataFrame In this article, we are going to see how to insert rows for missing dates in R Programming language. The padr package in R is used to make preparations of time series data using the pad() function. The package can be downloaded and installed into the working space using the following command : insta
2 min read
How to Create a Two-Variable Data Table in Excel? Two-Variable Data Table is a very significant tool for what-if data analysis. With the help of two-variable data tables, we can find all possible trends that can arrive by changing different values. For example, if we know the annual sales of a company, its percentage of expenses and growth. So, by
2 min read
Linear Interpolation in Excel - 10 Methods with Example Linear interpolation in Excel is a powerful method for estimating unknown values between two data points within a dataset. Imagine working with financial models, engineering calculations, or scientific data where you need to predict or fill in missing values with precision. Linear interpolation allo
15+ min read