Types & Uses of Moments in Statistics

fourth moment kurtosis

In statistics, moments are measures of the shape and variability of a data set. They are used to describe the location and dispersion of the data. There are several types of moments that can be calculated, each providing different information about the data set. Let’s take a look at some of these moments and how they can be used in statistical analysis.

What are moments in Statistics and what are their types?

In statistics, moments are an important tool used to measure the characteristics of a distribution. Moments can provide useful information about the spread, shape, and center of a distribution. 

The following are different types of moments:

  • First moment (Mean) – Mean is the most commonly used measure of central tendency. It gives us an idea of where the data is centered in a distribution.
  • Second moment (Variance) – Variance measures how to spread out or dispersed the data is around its mean value.
  • Third moment (Skewness) – Skewness measures the asymmetry of a distribution; it tells us if there are more values on one side than another side of the distribution (positively skewed, negatively skewed, or symmetric). A positive value of skew represents right-skewed distributions, a negative value of skew represents left-skewed distributions and a zero value of skewness indicates that the distribution is symmetric. The following picture demonstrates positive, negative, and zero-skewed datasets.

    zero and negative and positive skew

  • Fourth moment (Kurtosis) – Kurtosis measures the peakedness or flatness of a distribution; it tells us how much weight is at the center and tail ends of the distribution (leptokurtic, platykurtic, or mesokurtic). Positive kurtosis indicates that the data is more concentrated near the mean than in a normal distribution, while negative kurtosis indicates that the data is spread out more than in a normal distribution. The following picture demonstrates leptokurtic, platykurtic, and mesokurtic datasets.

    fourth moment kurtosis

    Python Code Examples to Calculate Moments

    The following is the Python code for creating a sample dataset whose moments will be calculated later. 

import numpy as np
import matplotlib.pyplot as plt

values = np.random.normal(0, 0.5, 10000)

plt.hist(values, 50)
plt.show()

The following plot gets printed representing the dataset.

The following python code represents moments for the above dataset.

import scipy.stats as sp

# First moment: Mean
mean = np.mean(values)

# Second moment: Variance
variance = np.var(values)

# Third moment: Skewness
skewness = sp.skew(values)

# Fourth moment: Kurtosis
kurtosis = sp.kurtosis(values)

print("Mean: ", mean, ", Variance: ", variance, "\nSkewness: ", skewness, ", Kurtosis: ", kurtosis)

The following gets printed. Note that skewness is negative which indicates that the data is left-tailed (long tail in the left). Also, kurtosis is negative which means that larger data is present in tails.

What is the real-world usage of Moments in Statistics?

Moments in statistics are commonly used to quantify various aspects of a distribution, such as its central tendency, skewness, and kurtosis. For example, the mean (or average), variance, and standard deviation are all moments that can be used to characterize a dataset. Moments can also be used to measure the degree of clustering or dispersion in a dataset. These parameters are useful for describing the shape of a data distribution, which is an important factor in many statistical analyses.

In addition to providing descriptive information about a data set, moments can also be used to determine if two or more datasets have similar characteristics. For example, it is possible to compare the means, variances, and other moments between two datasets to determine if they are similar or different. This technique is often employed in financial analyses where comparing stock prices over time requires knowledge of their distributions. Another common use case for moments is in quality control processes where moments are calculated from random product samples to ensure that production remains consistent over time.

Conclusion

In summary, moments provide valuable information about a given dataset which can be used to better understand trends and patterns over time as well as identify outliers or other unusual features within it. Knowing what kind of moments exist—mean, standard deviation, skewness/kurtosis—and being able to calculate them properly will go a long way towards helping you analyze datasets accurately and with confidence! As such, learning about moments should be an essential component for any statistician who wants to become proficient at their craft!

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking Enroll for 1-hour session on Business Problems to Analytics Use Cases
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