The **weighted standard deviation** is a useful way to measure the dispersion of values in a dataset when some values in the dataset have higher weights than others.

The formula to calculate a weighted standard deviation is:

where:

**N:**The total number of observations**M:**The number of non-zero weights**w**A vector of weights_{i}:**x**A vector of data values_{i}:**x:**The weighted mean

The easiest way to calculate a weighted standard deviation in R is to use the **wt.var()** function from the **Hmisc** package, which uses the following syntax:

#define data values x <- c(4, 7, 12, 13, ...) #define weights wt <- c(.5, 1, 2, 2, ...) #calculate weighted variance weighted_var <- wtd.var(x, wt) #calculate weighted standard deviation weighted_sd <- sqrt(weighted_var)

The following examples show how to use this function in practice.

**Example 1: Weighted Standard Deviation for One Vector**

The following code shows how to calculate the weighted standard deviation for a single vector in R:

library(Hmisc) #define data values x <- c(14, 19, 22, 25, 29, 31, 31, 38, 40, 41) #define weights wt <- c(1, 1, 1.5, 2, 2, 1.5, 1, 2, 3, 2) #calculate weighted variance weighted_var <- wtd.var(x, wt) #calculate weighted standard deviation sqrt(weighted_var) [1] 8.570051

The weighted standard deviation turns out to be **8.57**.

**Example 2: Weighted Standard Deviation for One Column of Data Frame**

The following code shows how to calculate the weighted standard deviation for one column of a data frame in R:

library(Hmisc) #define data frame df <- data.frame(team=c('A', 'A', 'A', 'A', 'A', 'B', 'B', 'C'), wins=c(2, 9, 11, 12, 15, 17, 18, 19), points=c(1, 2, 2, 2, 3, 3, 3, 3)) #define weights wt <- c(1, 1, 1.5, 2, 2, 1.5, 1, 2) #calculate weighted standard deviation of points sqrt(wtd.var(df$points, wt)) [1] 0.6727938

The weighted standard deviation for the points column turns out to be **0.673**.

**Example 3: Weighted Standard Deviation for Multiple Columns of Data Frame**

The following code shows how to use the sapply() function in R to calculate the weighted standard deviation for multiple columns of a data frame:

library(Hmisc) #define data frame df <- data.frame(team=c('A', 'A', 'A', 'A', 'A', 'B', 'B', 'C'), wins=c(2, 9, 11, 12, 15, 17, 18, 19), points=c(1, 2, 2, 2, 3, 3, 3, 3)) #define weights wt <- c(1, 1, 1.5, 2, 2, 1.5, 1, 2) #calculate weighted standard deviation of points and wins sapply(df[c('wins', 'points')], function(x) sqrt(wtd.var(x, wt))) wins points 4.9535723 0.6727938

The weighted standard deviation for the wins column is **4.954** and the weighted standard deviation for the points column is **0.673**.

**Additional Resources**

How to Calculate Weighted Standard Deviation in Excel

How to Calculate Standard Deviation in R

How to Calculate the Coefficient of Variation in R

How to Calculate the Range in R

So basically, we plot points on a graph with a value and determine what all those values equal and come to a median value giving a prediction with enough data to make an educated guess..