Using statistics: How to understand population distributions?

Kunal Jain 17 Apr, 2015 • 5 min read

One of the common queries, which I get on the blog is:

I am not a Mathematics / Statistics graduate. Can I still become a good business analyst?

or

I am not good at statistics. Can I still change my career to become a business analyst?

The simple answer to the question is – you can’t become a good analyst until you know statistics. However, you don’t need to be an expert in statistics to become a good business analyst.

So, you don’t need to understand the joke in the strip, in order to qualify as a business analyst:

xkcd_seashell

 

Two types of Business Analytics:

Business anlaytics can be divided in two classes – applied business analytics and theoretical business analytics. Here are definitions of the two streams:

  1. Applied Business Analytics – This is the work, where the emphasis is to solve a problem at hand. What matters is that you have a strategy / algorithm, which is better than what is happening currently. You deal with practical problems in this stream – messy data, missing values, bad data capture etc. More than 95% of times, you would typically use the algorithm and outputs straight from the tools. As long as you are aware of the assumptions in your model, can check whether they are holding good and interpret the output of the algorithms correctly, you are good for applied business analytics.
    • For example, if you know the assumptions in Linear regression and can interpret what is R-square and adjusted R-square, you would be good to apply Linear Regression.
  2. Theoretical Business Analytics – This is the research area in business analytics. When you have a problem, where current set of algorithms are already optimized and applying standard techniques would not provide you any further uplift. This is when, you need to get into statistical details of various algorithms and then improve them.

categories of business analytics

 

Please note that this is not a standard categorization of Business Analytics and it might be difficult to identify some projects in exact buckets. However, it is good enough to communicate the point that you can deal with most of the business analytics problems with basic knowledge of statistics.

What does an analyst need to know?

Now, that you understand the two classes of business analytics, here is some good news! You don’t need to be a statistician to practice applied business analytics.

So, what exactly do you need to know to become an applied business analytics practitioner? I thought why not run a series of articles explaining the basic concepts of statistics, an applied BA practitioner needs to know.

Please note that this series is not intended to be a thesis on statistics. Instead, it takes a very practical outlook to apply statistics to solve business problems.

 

Concepts to understand population distributions:

One of the first things a business analyst needs to do is understand various distributions of parameters and population.

One of the most frequently used method to understand distributions is to plot them using histograms. A histogram represents frequencies of various values through a plot in uniform buckets (popularly known as bins). In case of continuous variables, a histogram represents the probability distribution function (we will cover this later). If you want an example of how histogram is plotted, you can look at this video from Khanacademy. Here is how a typical histogram might look like:

Example of histogram. Source: Wikipedia

Example of histogram. Source: Wikipedia

There are 3 variety of measures, required to understand a distribution:

  • Measure of Central tendency
  • Measure of dispersion
  • Measure to describe shape of curve

Measures of Central tendency:

Measures of central tendencies are measures, which help you describe a population, through a single metric. For example, if you were to compare Saving habits of people across various nations, you will compare average Savings rate in each of these nations.

Following are the measures of central tendency:

  • Mean – or the average
  • Median – the value, which divides the population in two half
  • Mode – the most frequent value in a population

The following image illustrates how mean, median and mode would be placed in a couple of scenarios:

central_tendency

Among the three measures, mean is typically affected the most by Outliers (unusually high or low values), followed by the median and mode.

Measures of Dispersion:

Measures of dispersion reveal how is the population distributed around the measures of central tendency.

  • Range – Difference in the maximum and minimum value in the population
  • Quartiles – Values, which divide the populaiton in 4 equal subsets (typically referred to as first quartile, second quartile and third quartile)
  • Inter-quartile range – The difference in third quartile (Q3) and first quartile (Q1). By definition of quartiles, 50% of the population lies in the inter-quartile range.
  • Variance: The average of the squared differences from the Mean.
  • Standard Deviation: is square root of Variance
standard_deviation

Difference in distribution of 2 populations with same mean, median and mode. Source: Wikipedia

 

Measures to describe shape of distribution:

  • Skewness – Skewness is a measure of the asymmetry. Negatively skewed curve has a long left tail and vice versa.
  • Kurtosis – Kurtosis is a measure of the “peaked ness”. Distributions with higher peaks have positive kurtosis and vice-versa

skewness and kurtosis

 

A few practical tips to understand distributions better:

  • Use of box plots: Box plots are one of the easiest and most intuitive way to understand distributions. They show mean, median, quartiles and Outliers on single plot.

box plot

  • You can use box plots next to each other for various categories / segments of population to understand overlap / differences in the population. Following is an example of one such comparison (with illustrative data):

box_plot_comparisonIn this post, we looked use of statistics to plot and understand distributions of populations – first steps for any business analyst to do in a project. In the articles to follow in this series, we will look at use of confidence intervals, hypothesis testing, probabilities and measures to judge various predictive models. If you would want me to cover more topics, please let me know through comments below.

In the article next week (from baby steps in Python series), we will see how to look at these measures and distributions using Python on a Kaggle dataset.

If you like what you just read & want to continue your analytics learning, subscribe to our emailsfollow us on twitter or like our facebook page.

Kunal Jain 17 Apr 2015

Kunal is a post graduate from IIT Bombay in Aerospace Engineering. He has spent more than 10 years in field of Data Science. His work experience ranges from mature markets like UK to a developing market like India. During this period he has lead teams of various sizes and has worked on various tools like SAS, SPSS, Qlikview, R, Python and Matlab.

Frequently Asked Questions

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Responses From Readers

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Shreenag Haranhalli
Shreenag Haranhalli 22 Jul, 2014

Thank you for a lovely insight . I have just started my career as a data analyst so i would like to ask you are there any books or courses that i can do to enhance my statistics skills ?

Chirag Pradhan
Chirag Pradhan 22 Jul, 2014

Precise and to the point. Your blog is such a treasure trove. Looking forward to read more on this topic. Thanks Kunal

Parul
Parul 22 Jul, 2014

Thanks Kunal, helpful article, Can you explain same things using SAS ? If possible, please explain a bit more about kurtosis and what is the physical significance of it along with skewness ? Also, by looking at default output of means procedure in SAS, what story can we tell to the client ? Can you please answer this to make me understand how to interpret the results. I know we get N, min, max, std and mean as default but how to tell a story using these to the third person is what I want to understand . Any enlightment would be of great help.

Mahendra
Mahendra 23 Jul, 2014

Skewness and Kurtosis,if Both values close to 0 expect normal distribution. practically impossible to get 0.Distribution plays pivotal role for example if my dependant variable is having 97% zero that makes possions and 3% $amount making it continous it means distribution is composed of Poisson's (1) and gamma (2) somewhere between 1~2 . hence if i m predicting any value ,no algorithm can give me a clear output of Zero hence treatment require specific algorithm hope it gives some light thanks

Harsh Beria
Harsh Beria 24 Jul, 2014

I liked the article but there are some technical short comings in it. What you have shown is not a box plot but a modified box plot or a whisker plot. It would have been better if you specified lower and upper whisker length.

sujatha
sujatha 24 Jul, 2014

Hi, I am MCA Graguate. I would like to learn analytics. can i become a business Analytics / Data Analytics. Because totally differ from my curriculum and career. Do I get the job opportunity? how will be the exposure in this field. Can you please assist further on this. Thanks Sujatha.

Sujatha
Sujatha 25 Jul, 2014

Thanks Kunal

Alessandra
Alessandra 29 Jul, 2014

Udacity has some good statstics courses, free of charge.

Nitin Kumar
Nitin Kumar 30 Jul, 2014

The article above is very meaningful and very precise for any Statistic learners. Looking forward for some more articles on other part of this subject with some examples. Nitin

Fazal
Fazal 16 Aug, 2014

Hi, Kunal. I've just seen your post.Its really interesting.Do you have experience of applying predictive analytic for analysis. please give some insight into its current applications and related careers in business. I am waiting. Good luck, Fazal

Hassine Saidane
Hassine Saidane 06 Nov, 2014

Hi Kunal, Great intro to stats for beginners. Where can I find the follow up (next week) article (how to look at these measures) and articles on the use of confidence intervals, hypothesis testing, probabilities and measures to judge various predictive models, mentionned in this article? Thanks Hassine

SANAL KUMAR
SANAL KUMAR 26 Apr, 2015

i am studied spss recently . i have 54 years of age. i have last 15 years of expeirince in market research field except data analysis. I want a training for data analysis. Where can i get a chance. Please give a reply as early as possible. Thanking you SANAL KUMAR

Vinay Jain
Vinay Jain 02 Jun, 2016

I enjoyed reading this one. Thanks for posting such articles.

Gopal
Gopal 02 Nov, 2017

Sir, please post an article on fuzzy logic. Suppose I have 2 different datasets with different sizes and I want to match approximate string matching in R studio.then how can I do this? Please help if you can....

avi
avi 19 Apr, 2018

Hi Kunal sir, This is a wonderful article to provide stats and its usages in applied BA for a novice. I am looking forward to read more statistics term and how it is used in the world of data science. Please keep me posted.

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