Measures of Dispersion

Learn predictive, descriptive & inferential statistical analysis with Measures of Dispersion Course.

Instructor:

Dr. Abhinanda Sarkar
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1.5 Hrs

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About this Free Certificate Course

In this course, you will acquire skills in Measures of Dispersion. You will start this course by learning what statistics is and its data collection. Then we will jump to different types of statistical analysis such as predictive, descriptive and inferential. Then we will see analytics in excel and descriptive statistics in which you will be knowing about central tendency, dispersion and shape. Then, moving to next you will know about measures of location such as mean, median and mode. Lastly, you will be seeing the measures of dispersion by knowing the range of the data, variance and Standard deviation.

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Course Outline

Descriptive statistics

This section explains how data is described without having pre-conditioned assumptions or pre-built models into it. It explains descriptive analysis with various medical-line examples. 

Measures of Dispersion

This section describes the standard deviation by formulating to solve for it. It explains the relative tendency towards the most accurate solution through the derived observation. You will also learn to work with code in the Jupyter notebook to understand this better. You will also learn to graphically represent the observation, about data, and metadata in the later part of this section. 
 

 

Introduction to Statistics

Statistics is the discipline of science that deals with studying the collection of data, analyzing, interpreting, and presenting empirical data. It explores the variation in the set of data.

What is Statistics?
Data Collection for Statistics

Statistics uses many approaches to collect the data for statistical analysis. Direct observation, Experiments, and surveys are the most popular methods to obtain data for statistical analysis. The survey collects data from people through Gallup polls, pre-election polls, marketing surveys, and other forms. 

Types of Statistical Analysis

Descriptive Statistical analysis deals with organizing and interpreting data using graphical and numerical methods, such as Inferential Statistical Analysis, Predictive Analysis, Prescriptive Analysis, Casual Analysis, Mechanistic Analysis are the standard statistical analysis techniques used in Descriptive Statistics.

Analytics with Excel
Measures of Location
Variance and Standard Deviation

Our course instructor

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Dr. Abhinanda Sarkar

Faculty Director, Great Learning

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508.6K+ Learners
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17 Courses
Dr. Abhinanda Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. Dr. Sarkar received his B.Stat. and M.Stat. degrees from the Indian Statistical Institute (ISI) and a Ph.D. in Statistics from Stanford University. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT); been on the research staff at IBM; led Quality, Engineering Development, and Analytics functions at General Electric (GE); served as Associate Dean at the MYRA School of Business; and co-founded OmiX Labs.

Dr. Sarkar’s publications, patents, and technical leadership have been in applying probabilistic models, statistical data analysis, and machine learning to diverse areas such as experimental physics, computer vision, text mining, wireless networks, e-commerce, credit risk, retail finance, engineering reliability, renewable energy, and infectious diseases, His teaching has mostly been on statistical theory, methods, and algorithms; together with application topics such as financial modeling, quality management, and data mining.

Dr. Sarkar is a certified Master Black Belt in Lean Six Sigma and Design for Six Sigma. He has been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc). Over the years, he has designed and conducted numerous corporate training sessions for technology and business professionals. He is a recipient of the ISI Alumni Association Medal, IBM Invention Achievement Awards, and the Radhakrishan Mentor Award from GE India

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Measures of Dispersion

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Frequently Asked Questions

What do you mean by Measures of Dispersion?

In statistics, the Measures of Dispersion allows you to determine whether the data collected is homogeneous or heterogeneous. It helps you to interpret the variability of the data, and it will show you how scattered the variables are.   

 

What are the four Measures of Dispersion?

The four commonly used Measures of Dispersion include Range, Quartile Deviation, Average Deviation or Mean Deviation, Standard Deviation, and Variance. 

 

What are the examples of Dispersion measures?

Examples of Dispersion Measures include Standard Deviation, Interquartile Range (IQR), Range, Mean Absolute Difference, Median Absolute Deviation, Average Absolute Deviation, and Distance Standard Deviation

What is the use of Measures of Dispersion?

Measures of Dispersion are utilized for finding how to spread out the numbers are on the number line, and it is also known as the Measures of spread. The tabular form containing values provides you with the description and the function names. You can say that Range is the most straightforward measure of spread.  

 

Will I get a certificate after completing this Measures of Dispersion free course?

Yes, you will get a certificate of completion for Measures of Dispersion after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.

How much does this Measures of Dispersion course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Measures of Dispersion can get started with this course.

Is there any limit on how many times I can take this free course?

Once you enroll in the Measures of Dispersion course, you have lifetime access to it. So, you can log in anytime and learn it for free online.

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this free Measures of Dispersion course?

Great Learning Academy provides this Measures of Dispersion course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.

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Enrolling in any of the Great Learning Academy’s courses is just one step process. Sign-up for the course, you are interested in learning through your E-mail ID and start learning them for free online.

Will I have lifetime access to this free Measures of Dispersion course?

Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to.

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Measures of Dispersion

Before diving into Measures of Dispersion, it is better to get a brief introduction to statistics. Statistics is the part of science that helps many in developing and studying the methods for collecting, analyzing, interpreting, and presenting the data. Statistics is also known as the highly interdisciplinary domain. Statistics have proved to be beneficial in the field of science. If you level it up, its methods are prevalent among Data Science techniques. Statistics play a vital role in almost every field. From mathematics to Data Science, statistics are found to be very helpful in finding new methods to solve problems. Research work has given rise to various statistical approaches, which are now advantages in analyzing and manipulating data.

The field of statistics has two fundamental ideas: uncertainty and variation. You may have seen many situations in your life where the results are uncertain. Similarly, many of the solutions to scientific problems are also uncertain. Sometimes your solution is uncertain because the result is not yet determined. In other cases, uncertainty occurs because we are unaware of the already present solution. Probability is the Mathematical concept that talks about certainty and uncertainty, and it plays a significant role in predicting situations. If you repeat the same measuring process, the result will likely change.

Coming to the Dispersion in Statistics, it is the way of describing how to spread out the data set. Dispersion indicates the data being dispersed, stretched, or spread out into different categories. Dispersion helps you find the distributed value for your particular variable. Summarizing Dispersion in Statistics numeric data may vary if there is vary in any instance of average value assumption. Dispersion helps categorize the data sets based on their Dispersion criteria like ranging, standard deviation, and variance. It allows one to determine the quality of the data in an objectively quantifiable manner. It is seen that the Measures of Dispersion almost has the same unit as the quantity being measured. 

Several Measures of Dispersion allow you to get valuable insights from the data. They are:

  • Range

  • Variance

  • Standard Deviation

  • Skewness

  • IQR

Measures of Dispersion are divided into two main categories, which offer to measure the diverse nature of data. The two categories are:

  • Absolute Measure of Dispersion

  • Relative Measure of Dispersion

Absolute Measure of Dispersion

Absolute Measure of Dispersion showcases the same unit as the initial data set. It is expressed in terms of the average of the Dispersion quantities like Mean or Standard Deviation. Types of Absolute Measure of Dispersion include:

  • Range : The Range is considered the simplest form of Dispersion that provides the difference between the largest to the smallest value of the data variability.

  • Mean : Mean allows you to find the average. To find the average of your outcomes, add all of them and divide them by the total number of terms.

  • Variance : Variance can be found by finding the sum of the squared distances of each term in the mean distribution and dividing this sum by the total number of terms present.

  • Standard Deviation : The square root of the variance found will provide you with the Standard Deviation. Hence, it is clear that to find the Standard Deviation of any data, you must first calculate its variance.

  • Quartile : It divides the list of data into Quartiles.

  • Quartile Deviation : It is the measure of the difference between the upper and lower Quartile of the data. This Measure of Dispersion is also known as the Interquartile Range. 

  • Mean Deviation : Average Deviation or the Mean Deviation is found by computing the mean or median of the data. It is also represented as the arithmetic deviation of the different data that follows the central tendency.

Relative Measures of Dispersion

These relative Measures of Dispersion are unitless. It is mostly utilized to draw comparisons for the distribution of multiple data sets. Relative Measures of Dispersion are the same as the Absolute Measure of Dispersion, and the only difference is the measuring quantity. Relative Measures of Dispersion are the calculation of the coefficients of the Dispersions. Here, two data series are compared, but they differ widely in their average. It comes into the picture when two different series of coefficients are compared. 

  • Co-efficient of Range : It is the ratio of the difference between the largest and lowest terms of the distribution to the sum of these terms of the distribution.

  • Co-efficient of Standard Deviation : It is the ratio of the Standard Deviation to the Mean of the distribution of the terms.

  • Co-efficient of Quartile Deviation : It is the ratio of the difference between the upper and lower quartile to their sum.

  • Co-efficient of Mean Deviation : On computing the Mean or Median of the data, you will get the coefficient of Median Deviation.

The knowledge of these Dispersions is considered vital as they allow you to diversify the data and understand how it is spread out and maintained. You can find many Measures of Dispersion examples to help you understand these concepts better. You will understand when and where these Measures of Dispersion have importance in the data set value. To understand Measures of Dispersion better, enroll in Great Learning’s free Measures of Dispersion course and get in-depth knowledge of all the essential concepts. Complete all the modules introduced in the course successfully to earn free Measures of Dispersion certificate, enhance your resume, and grab better job opportunities. Enroll Today! 

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