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Each PANOSE 2.0 category value is a signed 16-bit number (from −32,768 to 32,767; only ranges between −10,000 and 10,000 are defined), where value zero (0) is considered to be the "normal" for the digit. For example, digit zero for the weight represents medium weight.
A font is a particular set of glyphs (character shapes), differentiated from other fonts in the same family by additional properties such as stroke weight, slant, relative width, etc. The CSS term font face is matched with "font"; it is decided by a combination of the font family and the additional properties. In both HTML and CSS, the list is ...
Weight designations in font names may differ in regard to the actual absolute stroke weight or density of glyphs in the font. Attempts to systematize a range of weights led to a numerical classification first used in 1957 by Adrian Frutiger with the Univers typeface: 35 Extra Light , 45 Light , 55 Medium or Regular , 65 Bold , 75 Extra Bold ...
In the first CSS specification, [2] authors specified font characteristics via a series of properties: font-family; font-style; font-variant; font-weight; font-size; All fonts were identified solely by name. Beyond the properties mentioned above, designers had no way to style fonts, and no mechanism existed to select fonts not present on the ...
The studentized bootstrap, also called bootstrap-t, is computed analogously to the standard confidence interval, but replaces the quantiles from the normal or student approximation by the quantiles from the bootstrap distribution of the Student's t-test (see Davison and Hinkley 1997, equ. 5.7 p. 194 and Efron and Tibshirani 1993 equ 12.22, p. 160):
Typically, this is the height of the letter x in the font (the source of the term), as well as the letters v, w, and z. (Curved letters such as a , c , e , m , n , o , r , s , and u tend to exceed the x-height slightly, due to overshoot ; i has a dot that tends to go above x-height.)
The best example of the plug-in principle, the bootstrapping method. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio ...
(The kerning units for a given font are the same as the units used to express the character widths in that font.) Most kerning adjustments are negative, and negative adjustments are generally larger than positive ones. Adjustments for different pairs within a given font can range from a tiny 2 to over 100 (when expressed as 1000 units/em).