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All 11 are necessary for life. The remaining elements are trace elements, of which more than a dozen are thought on the basis of good evidence to be necessary for life. [1] All of the mass of the trace elements put together (less than 10 grams for a human body) do not add up to the body mass of magnesium, the least common of the 11 non-trace ...
Air displacement plethysmography (ADP, also known as whole-body air displacement plethysmography) is a recognized and scientifically validated densitometric method to measure human body composition. ADP is based on the same principles as the gold standard method of hydrostatic weighing , but through a densitometric technique that uses air ...
Hydrostatic weighing, also referred to as underwater weighing, hydrostatic body composition analysis and hydrodensitometry, is a technique for measuring the density of a living person's body. It is a direct application of Archimedes' principle , that an object displaces its own volume of water.
The ideal percent of body mass which should be fat depends on an individual's sex, age, and physical activity. For example, a female thirty year old will have a different ideal fat percentage than a male thirty year old. An athlete will have a different ideal than a non athlete, and it can depend on the sport. [24]
Composition over inheritance (or composite reuse principle) in object-oriented programming (OOP) is the principle that classes should favor polymorphic behavior and code reuse by their composition (by containing instances of other classes that implement the desired functionality) over inheritance from a base or parent class. [2]
In males, mean percentage body fat ranged from 23% at age 16–19 years to 31% at age 60–79 years. In females, mean percentage body fat ranged from 32% at age 8–11 years to 42% at age 60–79 years. But it is important to recognise that women need at least 9% more body fat than men to live a normal healthy life. [2]
Dummy variables are commonly used in regression analysis to represent categorical variables that have more than two levels, such as education level or occupation. In this case, multiple dummy variables would be created to represent each level of the variable, and only one dummy variable would take on a value of 1 for each observation.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]