#calculate weighted standard deviation of points and wins 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) Example 3: Weighted Standard Deviation for Multiple Columns of Data Frame The weighted standard deviation for the points column turns out to be 0.673. #calculate weighted standard deviation of points The following code shows how to calculate the weighted standard deviation for one column of a data frame in R: library(Hmisc)ĭf <- data. Example 2: Weighted Standard Deviation for One Column of Data Frame The weighted standard deviation turns out to be 8.57. The following code shows how to calculate the weighted standard deviation for a single vector in R: library(Hmisc) Example 1: Weighted Standard Deviation for One Vector The following examples show how to use this function in practice. 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 The formula to calculate a weighted standard deviation is: 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.
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