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What they can do and how they can be used to benefit the car are easily defined. Composition also provides a more stable business domain in the long term as it is less prone to the quirks of the family members. In other words, it is better to compose what an object can do than extend what it is . [1]
The death of Akbar Salubiro was the first fully confirmed case of a reticulated python (or in fact any snake) killing and consuming an adult human, [7] as the process of retrieving the body from the python's stomach was documented by pictures and videos taken by witnesses. [8] [9] [10] [11]
In object-oriented computer programming, a null object is an object with no referenced value or with defined neutral (null) behavior.The null object design pattern, which describes the uses of such objects and their behavior (or lack thereof), was first published as "Void Value" [1] and later in the Pattern Languages of Program Design book series as "Null Object".
In physics, the n-body problem is the problem of predicting the individual motions of a group of celestial objects interacting with each other gravitationally. [1] Solving this problem has been motivated by the desire to understand the motions of the Sun , Moon , planets , and visible stars .
The Zen of Python is a collection of 19 "guiding principles" for writing computer programs that influence the design of the Python programming language. [1] Python code that aligns with these principles is often referred to as "Pythonic". [2] Software engineer Tim Peters wrote this set of principles and posted it on the Python mailing list in ...
Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling.Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model.
In statistics, overdispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on a given statistical model. A common task in applied statistics is choosing a parametric model to fit a given set of empirical observations. This necessitates an assessment of the fit of the chosen model.
MAPE puts a heavier penalty on negative errors, < than on positive errors. [9] As a consequence, when MAPE is used to compare the accuracy of prediction methods it is biased in that it will systematically select a method whose forecasts are too low.