Coefficient Of Skewness Software Method

Adult by biostatistician Karl Pearson, Pearson'south coefficient of skewness is a way to measure out the skewness in a sample dataset.

At that place are really two methods that tin can be used to calculate Pearson'southward coefficient of skewness:

Method ane: Using the Mode

Skewness = (Mean – Mode) / Sample standard deviation

Method two: Using the Median

Skewness = 3(Mean – Median) / Sample standard deviation

In full general, the 2d method is preferred because the mode is not ever a good indication of where the "central" value of a dataset lies and in that location tin can be more than than one way in a given dataset.

The following step-by-stride example shows how to calculate both versions of the Pearson's coefficient of skewness for a given dataset in Excel.

Step i: Create the Dataset

Start, allow's create the following dataset in Excel:

Stride 2: Summate the Pearson Coefficient of Skewness (Using the Mode)

Next, we can employ the following formula to calculate the Pearson Coefficient of Skewness using the mode:

Pearson's coefficient of skewness in Excel

The skewness turns out to exist1.295.

Step three: Calculate the Pearson Coefficient of Skewness (Using the Median)

We can also use the post-obit formula to calculate the Pearson Coefficient of Skewness using the median:

Pearson's coefficient of skewness in Excel using the median

The skewness turns out to be0.569.

How to Interpret Skewness

We translate the Pearson coefficient of skewness in the following means:

A value of 0 indicates no skewness. If we created a histogram to visualize the distribution of values in a dataset, it would exist perfectly symmetrical.
A positive value indicates positive skew or "right" skew. A histogram would reveal a "tail" on the right side of the distribution.
A negative value indicates a negative skew or "left" skew. A histogram would reveal a "tail" on the left side of the distribution.

In our previous example, the skewness was positive which indicates that the distribution of data values was positively skewed or "correct" skewed.

Boosted Resources

Check out this article for a nice explanation of left skewed vs. right skewed distributions.