How do I use VAR.S Function in Excel to calculate how much monthly revenue varies from the average?

To calculate how much monthly revenue varies from the average, use the formula: =VAR.S(B2:B13). Monthly Revenue Variability This formula returns: 155,062,500.

How can I optimize VAR.S Function in Excel performance?

To optimize VAR.S Function in Excel: 1) Use specific ranges instead of entire columns, 2) Avoid volatile functions within VAR.S Function in Excel, 3) Consider array formulas for bulk operations, 4) Cache results when possible. AskFormulas automatically applies optimization best practices.

What is the syntax for VAR.S Function in Excel?

The VAR.S Function in Excel syntax in Excel is: =VAR.S(number1, [number2], ...). It takes 2 parameter(s): number1 (required), number2 (optional).

How do I use VAR.S Function in Excel to measure manufacturing precision using variance?

To measure manufacturing precision using variance, use the formula: =VAR.S(C2:C51). Quality Control Precision This formula returns: 0.0529 mm².

Can VAR.S Function in Excel handle headers in Excel?

Yes, VAR.S Function in Excel can handle headers. For example: =VAR.S(B2:B13) will calculate how much monthly revenue varies from the average. This returns: 155,062,500

VAR.S Function in Excel

Calculate sample variance in Excel with VAR.S. Master variance analysis with syntax, examples, and solutions for statistical data analysis in spreadsheets.

Excel

Google Sheets

statistical

intermediate

Syntax Preview

Excel

Google Sheets

=VAR.S(number1, [number2], ...)

Quick Answer

TL;DR

VAR.S function VAR.S function calculates the variance of a sample dataset in Excel and Google Sheets. It measures how far numbers spread from their average value squared, providing a statistical measure of data dispersion. Use the syntax `=VAR.S(number1, [number2], ...)` where number1 is your required data range containing at least 2 values.

=VAR.S(number1, [number2], ...)

What is VAR.S?

Understanding Variance

## What is the VAR.S Function?

The VAR.S function is Excel's primary tool for calculating sample variance, a fundamental statistical measure that quantifies the squared average deviation from the mean in a dataset. Introduced in Excel 2010 to replace the older VAR function, it provides improved clarity by explicitly indicating it calculates for a sample rather than an entire population.

### Core Functionality

At its core, VAR.S operates using the unbiased sample variance formula. The function first calculates the mean (average) of all provided values, then measures how far each value deviates from this mean. These deviations are squared to eliminate negative values and emphasize larger differences, summed together, and finally divided by (n-1) where n is the sample size. The result is expressed in squared units of the original data.

This n-1 divisor (Bessel's correction) is what distinguishes sample variance from population variance. It provides an unbiased estimate when you're working with a sample that represents a larger population, accounting for the fact that sample data typically shows less variability than the full population. The mathematical formula is: Σ(x - x̄)² / (n-1).

### When to Use VAR.S

VAR.S is optimal for:

**Scenario 1:** When conducting statistical analysis that requires variance as an intermediate step. Many advanced statistical procedures (ANOVA, regression, hypothesis testing) require variance calculations rather than standard deviation. VAR.S provides the necessary variance estimate for sample data.

**Scenario 2:** For financial risk modeling where you need to calculate portfolio variance before converting to volatility. Variance is additive for independent variables, making it easier to combine risks from different sources before taking the square root for final standard deviation.

**Scenario 3:** In quality control when analyzing measurement precision. Variance allows you to partition total variability into different components (between-group vs within-group variation), which is essential for understanding sources of variation in manufacturing processes.

### Platform Compatibility

VAR.S works across platforms with complete compatibility:

- **Excel 2010+:** Full native support as the recommended variance function
- **Excel 365:** Enhanced with dynamic array support for complex calculations
- **Google Sheets:** Fully supported with identical syntax and behavior
- **Excel for Mac:** Complete parity with Windows version
- **Excel Online:** Full functionality available

Note: In older Excel versions (2007 and earlier), use the VAR function instead, though VAR.S is preferred in modern versions for clarity.

## Understanding Variance

Variance tells you how spread out your data values are from the mean, expressed in squared units. While this makes variance less intuitive than standard deviation, it has important mathematical properties that make it essential for statistical analysis.

### Relationship to Standard Deviation

Variance and standard deviation are closely related:
- **Standard Deviation** = √Variance
- **Variance** = (Standard Deviation)²

For example, if variance is 100, standard deviation is 10. If variance is 25, standard deviation is 5. Standard deviation is easier to interpret (same units as data), but variance is better for mathematical operations because it's additive.

### Why Squared Units Matter

Variance uses squared units (e.g., dollars², points², mm²) which seems odd but serves important purposes:
1. Eliminates negative deviations (all squared values are positive)
2. Emphasizes outliers more than standard deviation
3. Makes variance additive for independent variables
4. Simplifies mathematical derivations in statistical theory

### Sample vs Population Variance

The key difference between VAR.S (sample) and VAR.P (population) lies in what your data represents:

- **Use VAR.S when:** Your data is a subset of a larger group you're trying to understand. This is the most common scenario in business and research.
- **Use VAR.P when:** You have data for every single member of your population with no intention to generalize beyond this exact group.

In practice, use VAR.S unless you're absolutely certain you have complete population data. The formula difference is subtle but important: VAR.S divides by (n-1) while VAR.P divides by n.

Practical Examples

Common Errors and Solutions

#DIV/0!

Division by zero

Cause:

The function received fewer than 2 numerical values. Variance requires at least 2 data points to calculate dispersion from the mean. With only 1 value, there's no way to measure variability.

Solution:

Verify your range contains at least 2 numeric values. Check that cells aren't empty, contain errors, or have only text values. Use COUNT to verify: =IF(COUNT(A1:A10)>=2, VAR.S(A1:A10), "Need at least 2 values"). When working with filtered data, ensure at least 2 values remain visible after filtering.

Prevention:

Add conditional logic before calculating variance: =IF(COUNT(B2:B100)>=2, VAR.S(B2:B100), "Insufficient data for variance calculation")

Frequency: 40%

Example:

#VALUE!

Wrong value type

Cause:

The function encountered an argument that cannot be converted to a number, such as error values (#N/A, #REF!, #NAME?) within the data range, or text that doesn't represent numbers. VAR.S ignores text cells but will error if errors are present.

Solution:

Clean your data by removing or fixing error values first. Use IFERROR wrapper around source calculations to convert errors to blanks. Alternatively, use array formulas with IF to filter out problematic values: =VAR.S(IF(ISNUMBER(A1:A100),A1:A100)). Remember to press Ctrl+Shift+Enter for array formulas in older Excel versions.

Prevention:

Wrap source data in IFERROR: =VAR.S(IFERROR(A1:A10,"")). This converts errors to empty cells which VAR.S ignores. For more robust handling: =IF(COUNTA(IFERROR(A1:A10,""))>=2, VAR.S(IFERROR(A1:A10,"")), "Error in data")

Frequency: 30%

Example:

#NUM!

Invalid numerical operation

Cause:

All provided values are identical, resulting in zero variance. While mathematically valid (variance = 0 when all values are the same), some Excel versions may return #NUM! in edge cases, particularly with circular reference detection enabled or in specific calculation modes.

Solution:

Check if all values in your range are identical using =COUNTA(UNIQUE(A1:A10)). If only 1 unique value exists, variance of 0 is correct and expected. If this is unexpected, verify you're referencing the correct range and that data has been entered correctly. Consider whether zero variance is meaningful for your analysis.

Prevention:

Handle uniform datasets gracefully: =IF(COUNTA(UNIQUE(A1:A10))>1, VAR.S(A1:A10), 0). This explicitly returns 0 for uniform data instead of potentially triggering errors.

Frequency: 10%

Example:

Incorrect Result

Unexpected or seemingly wrong variance value

Cause:

Common causes include: (1) Confusing VAR.S with VAR.P - VAR.S gives slightly higher values; (2) Including header rows in the range; (3) Hidden rows containing outliers that dramatically increase variance; (4) References to entire columns picking up unexpected data; (5) Mixing different units or scales in the same calculation.

Solution:

1) Confirm you're using the correct function (VAR.S for samples, VAR.P for populations). 2) Exclude header rows from your range. 3) Unhide all rows to see complete data. 4) Use specific cell ranges (A2:A100) instead of entire columns (A:A). 5) Visually inspect data for outliers that legitimately increase variance. 6) Compare with manual calculation for a small subset to verify. 7) Check units are consistent.

Prevention:

Always use specific, well-defined ranges that exclude headers. Create supporting columns to identify outliers: =IF(ABS(A2-AVERAGE($A$2:$A$100))>2*SQRT(VAR.S($A$2:$A$100)),"Potential outlier",""). Review flagged values before finalizing analysis.

Frequency: 15%

Example:

Interpretation Confusion

Variance value seems too large or doesn't make sense

Cause:

Variance is in squared units, making it harder to interpret than standard deviation. For example, if measuring dollars, variance is in dollars². A variance of 10,000 dollars² sounds enormous but equals a standard deviation of $100, which may be quite reasonable depending on context.

Solution:

Convert variance to standard deviation for easier interpretation: =SQRT(VAR.S(A1:A100)). This returns the result in original units. For example, if variance is 625 mm², standard deviation is 25 mm. Use variance for calculations (it's additive and mathematically convenient) but report standard deviation for communication.

Prevention:

Always present both variance (for technical analysis) and standard deviation (for interpretation): "Variance = 10,000 dollars² (Standard Deviation = $100)". This helps stakeholders understand the actual magnitude of variability.

Frequency: 5%

Example:

Best Practices and Pro Tips

Variance vs Standard Deviation: When to Use Which

Use variance for mathematical calculations (it's additive, making portfolio variance or ANOVA easier) but present results as standard deviation for interpretation (original units are more intuitive). Formula: SD = √Variance. For example, calculate portfolio variance by adding individual asset variances, then convert final result to standard deviation for reporting.

Combining Multiple Ranges

You can calculate variance across non-contiguous ranges by listing them separately. This is particularly useful when comparing departments, time periods, or categories stored in different worksheet areas. VAR.S treats all ranges as one unified dataset, which is perfect for consolidated variance analysis.

Sample Size Matters Even More for Variance

Variance estimates are less stable than mean estimates, especially with small samples. With fewer than 20 data points, your variance estimate may be quite unreliable. For samples under 30, always report the sample size alongside variance. Larger samples (n>100) provide much more stable variance estimates that better represent population variability.

ANOVA and Variance Decomposition

Variance can be decomposed into components: Total Variance = Between-Group Variance + Within-Group Variance. This is the foundation of ANOVA (Analysis of Variance). Use VAR.S to calculate variances within each group, then compare to overall variance to understand how much variability is due to group differences vs random variation within groups.

Variance in Finance: Portfolio Risk

In portfolio management, variance is additive for uncorrelated assets. Total portfolio variance = w1²×σ1² + w2²×σ2² + 2×w1×w2×Cov(1,2), where w = weights, σ² = variances, and Cov = covariance. This property makes variance mathematically convenient for risk calculations, though you'll convert to standard deviation (volatility) for final reporting.

Excel vs Sheets Compatibility

VAR.S works identically in Excel (2010+) and Google Sheets with the same syntax and results. For older Excel versions (2007 and earlier), use VAR instead. In Google Sheets, VAR and VAR.S are equivalent. When sharing files between platforms, VAR.S ensures consistent behavior and clear communication that you're calculating sample variance.

Coefficient of Variation for Relative Variability

To compare variability between datasets with different scales or units, calculate the coefficient of variation (CV): (Standard Deviation / Mean) × 100 = (√Variance / Mean) × 100. This expresses variability as a percentage of the mean, making it scale-independent. CV = 15% means variability is 15% of average value.

Variance and the Empirical Rule

For normally distributed data, variance helps define probability ranges. With variance σ², approximately 95% of values fall within ±2σ of the mean (where σ = √variance). This means ±2×SQRT(VAR.S(data)) captures most data points. Values beyond ±3σ (99.7% threshold) are potential outliers deserving investigation.

Don't Mix Sample and Population Functions

Using VAR.S when you have population data (or VAR.P for sample data) leads to incorrect statistical conclusions. VAR.S always gives slightly higher values than VAR.P for the same data because it divides by n-1 instead of n. Use VAR.S (sample variance) unless you're absolutely certain you have every member of the population with no need to generalize.

Handling Outliers in Variance Calculations

Variance is heavily influenced by outliers because it squares deviations (a value 10 units away contributes 100 to variance). Before calculating variance, identify and investigate outliers using: =IF(ABS(A2-AVERAGE($A$2:$A$100))>3*SQRT(VAR.S($A$2:$A$100)),"Outlier","Normal"). Decide whether to include, exclude, or investigate outliers based on context and whether they represent valid data or errors.

VAR.S vs Related Functions

## VAR.S vs Related Functions

### Primary Alternatives

- **VAR.P:** Population variance - use only when you have complete data for an entire population with no intent to generalize. Returns a slightly smaller value than VAR.S for the same dataset because it divides by n instead of n-1. VAR.P underestimates variance when applied to sample data, leading to overly optimistic conclusions about data consistency.

- **STDEV.S:** Sample standard deviation - the square root of VAR.S. Returns results in original units rather than squared units, making it much easier to interpret. Use STDEV.S for communication and reporting; use VAR.S for mathematical calculations where additivity matters. Formula: STDEV.S = √VAR.S.

- **VAR:** Legacy function from Excel 2007 and earlier, equivalent to VAR.S but less explicit about calculating sample statistics. Use VAR.S in modern Excel for clarity and consistency with other statistical functions.

### Complementary Functions

- **AVERAGE:** Calculate the mean before interpreting variance. Variance measures spread around the mean, so you need both values for complete understanding. Report as: "Mean = 100, Variance = 625 (SD = 25)" to provide full picture of central tendency and dispersion.

- **MEDIAN and QUARTILE.INC:** These provide robust alternatives when data has outliers. Since variance squares deviations, it's highly sensitive to extreme values. If median differs significantly from mean, consider reporting interquartile range (Q3-Q1) alongside or instead of variance.

- **COVARIANCE.S:** Extends variance to measure how two variables vary together. Covariance is the foundation for correlation and is calculated similarly to variance but compares deviations of two variables simultaneously. Essential for portfolio analysis and multivariate statistics.

### Statistical Analysis Functions

- **F.TEST:** Uses variance to compare variability between two groups. Tests whether two samples have significantly different variances, which is a prerequisite for many other statistical tests like t-tests. Formula: F = VAR.S(group1) / VAR.S(group2).

- **CONFIDENCE.NORM:** Builds on variance to calculate confidence intervals. Uses the formula: CONFIDENCE.NORM(alpha, SQRT(VAR.S(data)), COUNT(data)) to determine margin of error around the sample mean.

- **STANDARDIZE:** Converts values to z-scores using variance: (value - mean) / SQRT(variance). This expresses each value in terms of how many standard deviations it is from the mean, enabling comparison across different scales.

### Advanced Applications

- **Portfolio Variance:** For financial analysis, combine individual asset variances to calculate total portfolio risk: Portfolio_Var = w1²×VAR.S(Asset1) + w2²×VAR.S(Asset2) + 2×w1×w2×COVARIANCE.S(Asset1, Asset2).

- **ANOVA (Analysis of Variance):** Decompose total variance into between-group and within-group components to test whether group means differ significantly. Calculate: Total_Var = Between_Group_Var + Within_Group_Var.

- **Regression Analysis:** In regression, variance helps calculate R² (coefficient of determination). R² = 1 - (Residual Variance / Total Variance), showing the proportion of variance explained by the model.

### Next Learning Steps

Master these related functions to build comprehensive statistical analysis capabilities:
1. Start with AVERAGE and VAR.S for basic descriptive statistics
2. Add STDEV.S for easier interpretation (convert squared units back to original scale)
3. Use COVARIANCE.S when analyzing relationships between variables
4. Apply F.TEST to compare variances between groups
5. Leverage CONFIDENCE.NORM for inferential statistics
6. Explore ANOVA techniques for comparing multiple groups
7. Build portfolio models combining VAR.S with COVARIANCE.S