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A simple smiley. This is a list of emoticons or textual portrayals of a writer's moods or facial expressions in the form of icons.Originally, these icons consisted of ASCII art, and later, Shift JIS art and Unicode art.
Meaning [1] Latin (or Neo-Latin) origin [1] a.c. before meals: ante cibum a.d., ad, AD right ear auris dextra a.m., am, AM morning: ante meridiem: nocte every night Omne Nocte a.s., as, AS left ear auris sinistra a.u., au, AU both ears together or each ear aures unitas or auris uterque b.d.s, bds, BDS 2 times a day bis die sumendum b.i.d., bid, BID
Meaning Origin language and etymology Example(s) bacillus: rod-shaped Latin baculus, stick Bacillus anthracis: bacteri-Pertaining to bacteria: Latin bacterium; Greek βακτήριον (baktḗrion), small staff bacteriophage, bactericide: balan-of the glans penis or glans clitoridis: Greek βάλανος (bálanos), acorn, glans balanitis: bas-
The theory of median-unbiased estimators was revived by George W. Brown in 1947: [8]. An estimate of a one-dimensional parameter θ will be said to be median-unbiased, if, for fixed θ, the median of the distribution of the estimate is at the value θ; i.e., the estimate underestimates just as often as it overestimates.
Scientific wild-ass guess (SWAG) is an American English slang term meaning a rough estimate made by an expert in the field, based on experience and intuition. It is similar to the slang word guesstimate , a portmanteau of guess and estimate .
Leibniz's notation for differentiation does not require assigning meaning to symbols such as dx or dy (known as differentials) on their own, and some authors do not attempt to assign these symbols meaning. [1] Leibniz treated these symbols as infinitesimals.
To estimate μ based on the first n observations, one can use the sample mean: T n = (X 1 + ... + X n)/n. This defines a sequence of estimators, indexed by the sample size n. From the properties of the normal distribution, we know the sampling distribution of this statistic: T n is itself normally distributed, with mean μ and variance σ 2 /n.
In general, with a normally-distributed sample mean, Ẋ, and with a known value for the standard deviation, σ, a 100(1-α)% confidence interval for the true μ is formed by taking Ẋ ± e, with e = z 1-α/2 (σ/n 1/2), where z 1-α/2 is the 100(1-α/2)% cumulative value of the standard normal curve, and n is the number of data values in that ...