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Pearson product-moment correlation coefficient - Wikipedia, the. In statistics, the Pearson product-moment correlation coefficient (/ˈpɪərsɨn/) (sometimes referred to as the PPMCC or PCC or Pearson's r) is a measure of the linear correlation (dependence) between two variables X and Y, giving a value between +1 and −1 inclusive, where 1 is total positive correlation, 0 is no correlation, and −1 is total negative correlation. It is widely used in the sciences as a measure of the degree of linear dependence between two variables. It was developed by Karl Pearson from a related idea introduced by Francis Galton in the 1880s.[1][2][3] Examples of scatter diagrams with different values of correlation coefficient (ρ) Several sets of (x, y) points, with the correlation coefficient of x and y for each set. Definition[edit] Pearson's correlation coefficient between two variables is defined as the covariance of the two variables divided by the product of their standard deviations.

For a population[edit] where: Then the formula for ρ can also be written as where and. 8 Tips for Crafting Metrics That Matter. Metrics are the marketer's microscope. They show him what his customers are actually doing, as opposed to what they say they are doing or intend to do. With proper metrics he can make decisions faster and more accurately. You can decide to measure anything, but what metrics matter and what ones are just for show? Here are some rules I hope will guide you toward creating meaningful metrics that help, rather than hinder, the decision-making process. Be Actionable If I had to give a one-sentence answer to the question "What metrics should I implement for my product? " Most of the tips below are meant to focus attention on this issue. Be Understandable and Trustworthy Do you understand what your metric measures? Trust is the important part.

Measure Results Does your metric measure customer behavior or a correlate of customer behavior? For example, if you want to know how good Twitter is for your business don't measure the number of positive tweets about your company. Understand the Downside. Metrics and product design. Above: Hollywood sequels follow from risk-averse design decisions, like the widely panned Godfather Part 3 The dangers of the metrics-driven design processMany readers of this blog are expert practitioners of metrics-driven product development, and with this audience in mind, my post today is on the dangers of going overboard with analytics. I think that this is an important topic because the metrics-driven philosophy has come to dominate the Facebook/OpenSocial ecosystem, with negative consequences. App developers have pursued short-term goals and easy money – leading to many copycat and uninspired products.

At the same time, it’s clear that A/B testing and metrics culture serves only to generate more data-points, and what you do with that data is up to you. Smart decisions made by entrepreneurs must still be employed to reach successful outcomes. So let’s talk about the dangers of being overly metrics-driven – here are a couple of the key issues that can come up: Let’s dive in deeper… Text Crit » Textual Criticism. String Similarity Metrics for Information Integration. String metric. A widespread example of a string metric is DNA sequence analysis and RNA analysis, which are performed by optimised string metrics to identify matching sequences. List of string metrics[edit] See also[edit] External links[edit] Porter Stemming Algorithm. Similarity Search in High Dimensions via Hashing. Levenshtein Distance.