Scattered notes on (retro) computing, data analysis, statistics, SEO, and things that change
The Poisson distribution is a discrete probability distribution that describes the number of events occurring in a fixed interval of time or area.
The Poisson distribution is useful for measuring how many events can occur within a given time horizon, such as the number of customers entering a shop in the next hour, or the number of pageviews on a website in the next minute, and so on.
Continue reading “Poisson Distribution: predicting leads, clicks and anomalies (with R and Python)”
After looking at the most famous discrete distribution, the Binomial, as well as the Poisson distribution and the Beta distribution, it is time to take a look at the geometric distribution.
The need I feel—the fruit of many years working in this field—is to affirm the decisive importance of basic scientific rigour in analysing traffic data, so that we can calibrate our SEO interventions with accuracy, and not merely “by gut feeling” (even though feelings do matter!).
The tools available to the SEO professional are countless, and yet it is undeniable that a sense of disappointment lingers within us. Too often we deal with data of apparent strategic importance that turn out, when put to the test, to be fallacious or imprecise—mere red herrings.
Two sites, two charts of daily sessions. The first swings by almost 400 sessions a day, the second by just 52: one would be tempted to call the first far less stable.
The opposite is true, and to notice it the mean is not enough: we need to know how far the data stray from it.
That is the job of measures of dispersion (or of variability): describing how much the observations spread around a central value, the mean or the median. Two datasets with the same centre can tell opposite stories, and these measures are what reveal it.
The techniques for measuring the variability of datasets are numerous. Among them, the most widely known (and most commonly used) are:
We will also visualise the concepts of central tendency and dispersion by revisiting skewness and introducing the concept of kurtosis.
Continue reading “Standard Deviation and CV: when the mean alone deceives you (SEO case studies)”
A random variable (also called a stochastic variable) is a variable that can take on different values depending on some random phenomenon. In many statistics textbooks it is simply abbreviated as r.v. It is a numerical value.
When probability values are assigned to all the possible numerical values of a random variable x, the result is a probability distribution.
In even simpler terms: a random variable is a variable whose values are each associated with a probability of being observed. The set of all possible values of a random variable and their associated probabilities is called a probability distribution. The sum of all probabilities is 1.
Continue reading “Probability Distributions: Discrete Distributions and the Binomial”