Scattered notes on (retro) computing, data analysis, statistics, SEO, and things that change
The Gini coefficient is a measure of the degree of inequality in a distribution, and is commonly used to measure income distribution.
These few words alone are enough to grasp the extraordinary importance of this index for economic and political studies, and why it is worth getting to know it a little more closely.
Continue reading “The Gini Index: What It Is, Why It Matters, and How to Compute It in R”
Contingency tables are used to evaluate the interaction between two categorical variables (qualitative). They are also called two-way tables or cross-tabulations.
Searching for relationships between two categorical variables is a very common goal for researchers. Think, for example, of the classic question that marketers ask: who is more likely to buy certain product categories, young or old people, men or women…
Continue reading “Contingency Tables and Conditional Probability”
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.
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.