Distributions that have a skewness value 1 are typically classified as "skewed." The closer skewness is to 0, the more symmetric its distribution will look. We can quantify how skewed our data is by using a measure aptly named skewness, which represents the magnitude and direction of the asymmetry of data: large negative values indicate a long left-tail distribution, and large positive values indicate a long right-tail distribution. In the plot below, we can see that the data is distributed fairly evenly with one outlier in the top-right corner of the plot. This plot can help us easily identify potentially interesting clusters of data points. Differences in positions on the y-axis are created by a “ jitter” which allows overlapping points to be separated in the plot. In these plots, the x-axis represents a data point’s value, while the y-axis holds no meaning. To investigate the distribution of data, I like to look at four charts: dot-diagrams, box-plots, histograms, and the empirical cumulative distribution function.ĭot-diagram: This is possibly the most basic representation of your data-each data point is represented by a dot. In order to answer these questions, we’ll look at Crunchbase data, which among other information, contains records of startup funding amounts as of Feb 5, 2014. We’ll be able to answer important questions such as: 1.) How much funding does the typical startup receive? 2.) Do most startups raise money within a narrow range, or is funding spread widely? 3.) What is the minimum and maximum funding we should expect? Four charts for data exploration To get a sense of how much funding to expect, we’d like to understand how much funding other startups have recently received and investigating the distribution of startup funding is a great place to start. Let’s say we’re going after some funding for our new and exciting startup that makes monocles for dogs ( oh my, this actually exists). To understand the importance of visualizing data distributions, let’s try and answer some real-world questions. “Data distributions can surface meaningful patterns, trends or significant errors in your data.” ![]() How logarithmic scales help with skewed data ![]() This is why after wrangling together the data I need for an analysis, my first step is always to look at how my data is distributed. Exploring the distribution of data can surface meaningful patterns, trends, or significant errors in data that simple summary statistics like mean and median cannot capture.
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