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Personal data, also known as personal information or personally identifiable information (PII), [1] [2] [3] is any information related to an identifiable person.. The abbreviation PII is widely used in the United States, but the phrase it abbreviates has four common variants based on personal or personally, and identifiable or identifying.
Euler's identity is considered to be an exemplar of mathematical beauty as it shows a profound connection between the most fundamental numbers in mathematics. In addition, it is directly used in a proof [ 3 ] [ 4 ] that π is transcendental , which implies the impossibility of squaring the circle .
Thus, it is often called Euler's phi function or simply the phi function. In 1879, J. J. Sylvester coined the term totient for this function, [14] [15] so it is also referred to as Euler's totient function, the Euler totient, or Euler's totient. Jordan's totient is a generalization of Euler's. The cototient of n is defined as n − φ(n).
These mechanisms center on two HIPAA de-identification standards – Safe Harbor and the Expert Determination Method. Safe harbor relies on the removal of specific patient identifiers (e.g. name, phone number, email address, etc.), while the Expert Determination Method requires knowledge and experience with generally accepted statistical and ...
Personal Identifiers (PID) are a subset of personally identifiable information (PII) data elements, which identify an individual and can permit another person to "assume" that individual's identity without their knowledge or consent. [1] PIIs include direct identifiers (name, social security number) and indirect identifiers (race, ethnicity ...
The number π (/ p aɪ /; spelled out as "pi") is a mathematical constant, approximately equal to 3.14159, that is the ratio of a circle's circumference to its diameter.It appears in many formulae across mathematics and physics, and some of these formulae are commonly used for defining π, to avoid relying on the definition of the length of a curve.
is the number of collisions made (in ideal conditions, perfectly elastic with no friction) by an object of mass m initially at rest between a fixed wall and another object of mass b 2N m, when struck by the other object. [1] (This gives the digits of π in base b up to N digits past the radix point.)
In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or r φ) is a measure of association for two binary variables.. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.