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The [5 dl 2 dl 1 dl 2 dl] represents a dash-dot line. There are 5 units of line (the dash) followed by 2 units of empty space, 1 unit of line (the dot), 2 more units of empty space, and then it starts over again. 0.5 0.5 0.5 represents the color gray. /LTb is the graph's border, and /LTa is for the zero axes. [9]
The graph of the constant function y = c is a horizontal line in the plane that passes through the point (0, c). [2] In the context of a polynomial in one variable x, the constant function is called non-zero constant function because it is a polynomial of degree 0, and its general form is f(x) = c, where c is nonzero.
The {{Graphical timeline}} template allows representations of extensive timelines. The template offers complex formatting and labeling options to control the output. Typically, each use is made into its own template, and the template is then transcluded into the article. See an example here, and an example of it being used in an article here.
linewidths: different line widths may be defined for each series of data with csv, if set to 0 with "showSymbols" results with points graph, eg.: linewidths=1, 0, 5, 0.2; showSymbols: show symbol on data point for line graphs, if a number is provided, the symbol size (default 2.5) may be defined for each data series, eg.: showSymbols=1, 2, 3, 4
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A contour line (also isoline, isopleth, isoquant or isarithm) of a function of two variables is a curve along which the function has a constant value, so that the curve joins points of equal value. [1] [2] It is a plane section of the three-dimensional graph of the function (,) parallel to the (,)-plane. More generally, a contour line for a ...
A scatter plot, also called a scatterplot, scatter graph, scatter chart, scattergram, or scatter diagram, [2] is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. If the points are coded (color/shape/size), one additional variable can be displayed.
The normal distribution is NOT assumed nor required in the calculation of control limits. Thus making the IndX/mR chart a very robust tool. This is demonstrated by Wheeler using real-world data [4], [5] and for a number of highly non-normal probability distributions.