By Philipp K. Janert
Accumulating facts is comparatively effortless, yet turning uncooked info into anything priceless calls for that you just know the way to extract accurately what you would like. With this insightful ebook, intermediate to skilled programmers drawn to info research will examine concepts for operating with information in a enterprise atmosphere. you will the way to examine facts to find what it comprises, how you can seize these principles in conceptual types, after which feed your realizing again into the association via enterprise plans, metrics dashboards, and different functions. alongside the best way, you will scan with techniques via hands-on workshops on the finish of every bankruptcy. chiefly, you will find out how to take into consideration the implications you must in attaining - instead of depend upon instruments to imagine for you.
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Additional info for Data Analysis with Open Source Tools: A hands-on guide for programmers and data scientists
I divided the horizontal axis into 60 bins of 50 milliseconds width and then counted the number of events in each bin. info 15 What does the histogram tell us? We observe a rather sharp cutoff at a nonzero value on the left, which means that there is a minimum completion time below which no request can be completed. Then there is a sharp rise to a maximum at the “typical” response time, and finally there is a relatively large tail on the right, corresponding to the smaller number of requests that take a long time to process.
In the latter case we are using the width of the standard Gaussian distribution as a unit. You can convince yourself that this is really true by realizing that −1 (y) is the inverse of the Gaussian distribution function (x). Now ask yourself: what units is x measured in? We use the same units for the horizontal axis of a Gaussian probability plot. These units are sometimes called probits. ) Beware of confused and confusing explanations of this point elsewhere in the literature. There is one more technical detail that we need to discuss: to produce a probability plot, we need not only the data itself, but for each point xi we also need its quantile yi (we will discuss quantiles and percentiles in more detail later in this chapter).
Info 33 each other, we sum the square of the individual deviations and then take the mean of the square deviations. ) s2 = = 1 n 1 n (xi − m)2 i xi2 − m 2 i The quantity s 2 calculated in this way is known as the variance and is the more important quantity from a theoretical point of view. But as a measure of the spread of a distribution, we are better off using its square root, which is known as the standard deviation. Why take the square root? Because then both measure for the location, and the measure for the spread will have the same units, which are also the units of the actual data.