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A base-10 log scale is used for the Y-axis of the bottom left graph, and the Y-axis ranges from 0.1 to 1000. The top right graph uses a log-10 scale for just the X-axis, and the bottom right graph uses a log-10 scale for both the X axis and the Y-axis. Presentation of data on a logarithmic scale can be helpful when the data:
A log–log plot of y = x (blue), y = x 2 (green), and y = x 3 (red). Note the logarithmic scale markings on each of the axes, and that the log x and log y axes (where the logarithms are 0) are where x and y themselves are 1. Comparison of linear, concave, and convex functions when plotted using a linear scale (left) or a log scale (right).
The log–linear type of a semi-log graph, defined by a logarithmic scale on the y-axis (vertical), and a linear scale on the x-axis (horizontal). Plotted lines are: y = 10 x (red), y = x (green), y = log(x) (blue). The linear–log type of a semi-log graph, defined by a logarithmic scale on the x axis, and a linear scale on the y axis. Plotted ...
X-axis: The abundance rank. The most abundant species is given rank 1, the second most abundant is 2 and so on. Y-axis: The relative abundance. Usually measured on a log scale, this is a measure of a species abundance (e.g., the number of individuals) relative to the abundance of other species.
English: S&P 500 Index Logarithmic Chart's Interesting Features. While S&P 500 data to linear plot scale is good for analysis of a span of 2 or 3 years, beyond that a logarithmic S&P 500 chart is best. This is because it gives the same Y or vertical displacement for a certain percentage move up or down regardless of date.
Even in these cases, the log scale can make the data less apparent to the eye. Often the reason for the use of log scales is that the graph's author wishes to display vastly different scales on the same axis. Without log scales, comparing quantities such as 1000 (10 3) versus 10 9 (1,000,000,000) becomes visually impractical. A graph with a log ...
English: The log–linear type of a semi-log graph, defined by a logarithmic scale on the y-axis (vertical), and a linear scale on the x-axis (horizontal).
The MA plot puts the variable M on the y-axis and A on the x-axis and gives a quick overview of the distribution of the data. In many microarray gene expression experiments, an underlying assumption is that most of the genes would not see any change in their expression; therefore, the majority of the points on the y -axis ( M ) would be located ...