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In hydrology, crest is the highest level above a certain point (the datum point, or reference point) that a river will reach in a certain amount of time. This term is usually limited to a flooding event and from ground level.
A crest is a point on a surface wave where the displacement of the medium is at a maximum. A trough is the opposite of a crest, so the minimum or lowest point of the wave. When the crests and troughs of two sine waves of equal amplitude and frequency intersect or collide, while being in phase with each other, the result is called constructive ...
Significant wave height H m0, defined in the frequency domain, is used both for measured and forecasted wave variance spectra.Most easily, it is defined in terms of the variance m 0 or standard deviation σ η of the surface elevation: [6] = =, where m 0, the zeroth-moment of the variance spectrum, is obtained by integration of the variance spectrum.
Crest factor is a parameter of a waveform, such as alternating current or sound, showing the ratio of peak values to the effective value. In other words, crest factor indicates how extreme the peaks are in a waveform. Crest factor 1 indicates no peaks, such as direct current or a square wave. Higher crest factors indicate peaks, for example ...
Example graph of stream stages showing Action Stage, Flood Stage, Moderate Stage, Major Stage, and Record Stage on a river.. Flood stage is the water level, as read by a stream gauge or tide gauge, for a body of water at a particular location, measured from the level at which a body of water threatens lives, property, commerce, or travel. [1]
¯ = sample mean of differences d 0 {\displaystyle d_{0}} = hypothesized population mean difference s d {\displaystyle s_{d}} = standard deviation of differences
Random variables are usually written in upper case Roman letters, such as or and so on. Random variables, in this context, usually refer to something in words, such as "the height of a subject" for a continuous variable, or "the number of cars in the school car park" for a discrete variable, or "the colour of the next bicycle" for a categorical variable.
In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets. They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part.