Variance is a statistical measure that assesses the spread or dispersion of data. It helps us understand how far each data point deviates from the mean (average) of the data set. In Microsoft Excel, calculating variance is a straightforward process that involves using the VAR function, a builtin statistical function. This guide will walk you through the steps of calculating variance in Excel, providing a clear and friendly explanation for nontechnical users.
In statistics, variance is a key measure of variability. It allows us to quantify the extent to which data points are spread out from the mean. A low variance indicates that the data is clustered closely around the mean, while a high variance indicates that the data is more dispersed. Understanding variance is crucial for data analysis, as it helps in making inferences about the population from which the data was sampled.
To calculate variance in Excel, we will use the VAR function. This function takes a range of cells as its input and returns the variance of the data in that range. We will provide stepbystep instructions in the following sections, making sure to explain each step clearly and concisely.
How to Calculate Variance in Excel
Calculating variance in Excel is a simple process using the VAR function. Here are 8 important points to remember:
 Use the VAR Function
 Select the Data Range
 Exclude Text and Blank Cells
 Use the Sample Variance Option
 Understand the Variance Result
 Interpret the Variance Value
 Compare Variances
 Use Variance for Statistical Analysis
Remember these points to accurately calculate and interpret variance in Excel for your data analysis needs.
Use the VAR Function
To calculate variance in Excel, we will use the VAR function. This function is a builtin statistical function that calculates the variance of a range of cells. The syntax of the VAR function is as follows:
=VAR(number1, [number2], ...)
Where:

number1
: This is the first number or range of cells that you want to calculate the variance of. 
number2, ...
: These are additional numbers or ranges of cells that you want to include in the variance calculation. You can include up to 255 arguments in total.
For example, let’s say we have a range of cells A1:A10 that contains a set of data. To calculate the variance of this data, we would enter the following formula into a cell:
=VAR(A1:A10)
When we press Enter, Excel will return the variance of the data in the A1:A10 range. The variance value will be a positive number, and it will represent the average of the squared differences between each data point and the mean of the data.
The VAR function has an additional parameter called “sample” that you can use to specify whether you want to calculate the variance of the entire population or just a sample of the population. By default, the VAR function calculates the variance of the entire population. However, if you want to calculate the variance of a sample, you can set the “sample” parameter to TRUE. This will tell Excel to divide the sum of the squared differences by n1 instead of n, where n is the number of data points in the sample.
Select the Data Range
The first step in calculating variance in Excel is to select the range of cells that contains the data you want to analyze. This range can include numbers, but it should not include text or blank cells.

Select Contiguous Cells:
The data range should consist of contiguous cells. This means that the cells should be adjacent to each other, without any gaps or empty cells in between.

Exclude Text and Blank Cells:
Make sure that the data range does not include any text or blank cells. Text and blank cells can interfere with the variance calculation and produce inaccurate results.

Use Cell References or Range Names:
When selecting the data range, you can use cell references (e.g., A1:A10) or range names (e.g., “SalesData”). Range names make it easier to refer to a specific range of cells, especially if the range is large or located in a different worksheet.

Select the Entire Column or Row:
If you want to calculate the variance of an entire column or row, you can simply click on the column header or row number to select the entire column or row.
Once you have selected the data range, you can proceed to the next step, which is to use the VAR function to calculate the variance.
Exclude Text and Blank Cells
It is important to exclude text and blank cells from the data range when calculating variance in Excel. Text and blank cells can interfere with the variance calculation and produce inaccurate results.

Text Cells:
Text cells contain nonnumeric characters, such as letters, symbols, or spaces. These cells cannot be included in the variance calculation because they do not represent numerical values.

Blank Cells:
Blank cells are cells that do not contain any data. These cells are also excluded from the variance calculation because they do not contribute to the variability of the data.

Use the TRIM Function:
If your data range contains cells with leading or trailing spaces, you can use the TRIM function to remove the spaces and ensure that only the numeric values are included in the variance calculation.

Filter the Data:
Another way to exclude text and blank cells is to filter the data range. You can use the Filter feature in Excel to display only the cells that contain numeric values.
By excluding text and blank cells from the data range, you can ensure that the variance calculation is accurate and represents the true variability of the data.
Use the Sample Variance Option
The VAR function in Excel has an additional parameter called “sample” that you can use to specify whether you want to calculate the variance of the entire population or just a sample of the population. By default, the VAR function calculates the variance of the entire population. However, if you want to calculate the variance of a sample, you can set the “sample” parameter to TRUE.
There are two main reasons why you might want to calculate the sample variance instead of the population variance:
 Limited Data: When you only have a sample of the population, you cannot calculate the population variance directly. In this case, you can use the sample variance to estimate the population variance.
 Statistical Inference: In statistical inference, we often use the sample variance to make inferences about the population variance. For example, we might use the sample variance to test the hypothesis that the population variance is equal to a certain value.
To use the sample variance option in Excel, simply add the TRUE argument to the VAR function. For example, the following formula calculates the sample variance of the data in the range A1:A10:
=VAR.S(A1:A10)
When you press Enter, Excel will return the sample variance of the data. The sample variance will be a positive number, and it will represent the average of the squared differences between each data point and the sample mean.
It is important to note that the sample variance is always an estimate of the population variance. The accuracy of the estimate depends on the sample size. The larger the sample size, the more accurate the estimate will be.
Understand the Variance Result
Once you have calculated the variance of your data, it is important to understand what the result means. The variance is a measure of the spread or dispersion of the data. It tells you how much the data points vary from the mean.

Positive Variance:
A positive variance indicates that the data points are spread out from the mean. This means that some data points are above the mean and some data points are below the mean.

Zero Variance:
A variance of zero indicates that all of the data points are equal to the mean. This means that there is no variability in the data.

Large Variance:
A large variance indicates that the data points are spread out widely from the mean. This means that there is a lot of variability in the data.

Small Variance:
A small variance indicates that the data points are clustered closely around the mean. This means that there is not much variability in the data.
The variance can also be used to calculate the standard deviation, which is a measure of the average distance of the data points from the mean. The standard deviation is the square root of the variance.
Interpret the Variance Value
Once you understand what the variance is, you can start to interpret the variance value for your data. Here are a few things to consider:

Compare the Variance to Other Data Sets:
You can compare the variance of your data set to the variance of other data sets to see how your data compares. For example, you might compare the variance of your sales data to the variance of your competitor’s sales data.

Look for Trends:
You can also look for trends in the variance over time. For example, you might plot the variance of your sales data over time to see if there is a trend of increasing or decreasing variance.

Use the Variance to Make Decisions:
The variance can also be used to make decisions. For example, you might use the variance to decide whether or not to launch a new product. If the variance of the sales data for a new product is high, it means that there is a lot of uncertainty about how well the product will sell.

Limitations of Variance:
It is important to note that the variance is just one measure of variability. There are other measures of variability that you might want to consider, such as the range and the standard deviation.
By interpreting the variance value, you can gain insights into the variability of your data and make better decisions.
Compare Variances
Once you have calculated the variance of two or more data sets, you can compare the variances to see if there is a significant difference between them. This can be useful for determining whether or not two data sets are from the same population.

FTest:
The Ftest is a statistical test that can be used to compare the variances of two data sets. The Ftest statistic is calculated by dividing the variance of the first data set by the variance of the second data set. If the Ftest statistic is greater than 1, then there is a significant difference between the variances of the two data sets.

Levene’s Test:
Levene’s test is another statistical test that can be used to compare the variances of two or more data sets. Levene’s test statistic is calculated by comparing the absolute deviations of the data points from their respective medians. If the Levene’s test statistic is significant, then there is a significant difference between the variances of the data sets.

Bartlett’s Test:
Bartlett’s test is a statistical test that can be used to compare the variances of three or more data sets. Bartlett’s test statistic is calculated by comparing the natural logarithms of the variances of the data sets. If the Bartlett’s test statistic is significant, then there is a significant difference between the variances of the data sets.

Graphical Comparison:
You can also compare the variances of two or more data sets graphically. For example, you can create a box plot or a scatter plot to visualize the distributions of the data sets. If the distributions are significantly different, then the variances of the data sets are likely to be different as well.
By comparing variances, you can gain insights into the variability of your data and make better decisions.
Use Variance for Statistical Analysis
Variance is a fundamental measure of variability that is used in a wide variety of statistical analyses. Here are a few examples of how variance is used in statistical analysis:

Hypothesis Testing:
Variance is used in hypothesis testing to test the hypothesis that the variance of a population is equal to a certain value. This type of hypothesis test is often used to determine whether or not two data sets are from the same population.

Confidence Intervals:
Variance is used to construct confidence intervals for the population variance. A confidence interval is a range of values that is likely to contain the true value of the population variance.

Regression Analysis:
Variance is used in regression analysis to measure the amount of variability in the dependent variable that is explained by the independent variable(s). The variance of the residuals (the difference between the observed values and the predicted values) is used to assess the goodness of fit of the regression model.

Analysis of Variance (ANOVA):
Variance is used in ANOVA to test the hypothesis that the means of two or more groups are equal. ANOVA compares the variance between groups to the variance within groups to determine if there is a significant difference between the groups.
These are just a few examples of how variance is used in statistical analysis. Variance is a powerful tool that can be used to gain insights into the variability of data and to make better decisions.
FAQ
Here are some frequently asked questions about using a calculator to calculate variance:
Question 1: What is the formula for calculating variance?
Answer: The formula for calculating variance is:
Variance = Σ(x  μ)^2 / (n  1)
Where:

Σ
is the sum of all the values 
x
is a data point 
μ
is the mean of the data set 
n
is the number of data points
Question 2: How do I calculate variance using a calculator?
Answer: To calculate variance using a calculator, you can follow these steps:
 Enter the data points into the calculator.
 Calculate the mean of the data set.
 Subtract the mean from each data point.
 Square each of the differences.
 Add up the squared differences.
 Divide the sum of the squared differences by
(n  1)
.  The result is the variance.
Question 3: What is the difference between variance and standard deviation?
Answer: Variance and standard deviation are both measures of variability. Variance is the average of the squared differences between each data point and the mean. Standard deviation is the square root of the variance.
Question 4: When should I use variance instead of standard deviation?
Answer: Variance is often used in statistical calculations, such as hypothesis testing and regression analysis. Standard deviation is often used to describe the spread of data in a more easily understandable way.
Question 5: What is a good variance?
Answer: There is no such thing as a “good” or “bad” variance. The value of the variance depends on the context in which it is being used.
Question 6: How can I reduce the variance of my data?
Answer: There are a few ways to reduce the variance of your data. One way is to remove outliers from the data set. Another way is to increase the sample size.
Closing Paragraph: These are just a few of the most frequently asked questions about using a calculator to calculate variance. If you have any other questions, please consult a statistician or other expert.
Now that you know how to calculate variance, here are a few tips for using variance effectively:
Tips
Here are a few practical tips for using variance effectively:
Tip 1: Understand the concept of variance.
Before you can use variance effectively, it is important to understand what it is and how it is calculated. Variance is a measure of how spread out your data is from the mean. A low variance indicates that your data is clustered closely around the mean, while a high variance indicates that your data is more spread out.
Tip 2: Use variance to identify outliers.
Variance can be used to identify outliers in your data set. Outliers are data points that are significantly different from the rest of the data. Outliers can be caused by errors in data collection or entry, or they may be legitimate data points that represent extreme values. By identifying outliers, you can remove them from your data set and recalculate the variance to get a more accurate measure of variability.
Tip 3: Use variance to compare different data sets.
Variance can be used to compare the variability of two or more data sets. This can be useful for determining whether or not the data sets are from the same population. For example, you could use variance to compare the sales data of two different products to see if they have the same level of variability.
Tip 4: Use variance to make decisions.
Variance can be used to make decisions about your data. For example, you could use variance to decide whether or not to launch a new product. If the variance of the sales data for a new product is high, it means that there is a lot of uncertainty about how well the product will sell. This information could help you decide whether or not to launch the product.
Closing Paragraph: By following these tips, you can use variance effectively to gain insights into your data and make better decisions.
Conclusion: Variance is a powerful statistical tool that can be used to gain insights into the variability of data and to make better decisions. By understanding the concept of variance and using it effectively, you can improve your data analysis skills and make better use of your data.
Conclusion
Summary of Main Points:
 Variance is a measure of how spread out your data is from the mean.
 Variance can be calculated using a calculator or a spreadsheet program.
 Variance can be used to identify outliers, compare different data sets, and make decisions.
 There are a few tips for using variance effectively, such as understanding the concept of variance, using variance to identify outliers, and using variance to compare different data sets.
Closing Message:
Variance is a powerful statistical tool that can be used to gain insights into your data and make better decisions. By understanding the concept of variance and using it effectively, you can improve your data analysis skills and make better use of your data.
Whether you are a student, a researcher, or a business professional, variance is a valuable tool that can help you to make sense of your data and make better decisions.