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Semi-log plot of the Internet host count over time shown on a logarithmic scale. A logarithmic scale (or log scale) is a method used to display numerical data that spans a broad range of values, especially when there are significant differences between the magnitudes of the numbers involved.
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).
Similarly, likelihoods are often transformed to the log scale, and the corresponding log-likelihood can be interpreted as the degree to which an event supports a statistical model. The log probability is widely used in implementations of computations with probability, and is studied as a concept in its own right in some applications of ...
On a semi-log plot the spacing of the scale on the y-axis (or x-axis) is proportional to the logarithm of the number, not the number itself. It is equivalent to converting the y values (or x values) to their log, and plotting the data on linear scales. A log–log plot uses the logarithmic scale for both axes, and hence is not a semi-log plot.
When a real number like .007 is denoted alternatively by 7.0 × 10 —3 then it is said that the number is represented in scientific notation.More generally, to write a number in the form a × 10 b, where 1 <= a < 10 and b is an integer, is to express it in scientific notation, and a is called the significand or the mantissa, and b is its exponent. [3]
However, log-ratios are often used for analysis and visualization of fold changes. The logarithm to base 2 is most commonly used, [8] [9] as it is easy to interpret, e.g. a doubling in the original scaling is equal to a log 2 fold change of 1, a quadrupling is equal to a log 2 fold change of 2 and so on.
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Assuming a full-scale sine wave signal (that is, the quantizer is designed such that it has the same minimum and maximum values as the input signal), the quantization noise approximates a sawtooth wave with peak-to-peak amplitude of one quantization level [11] and uniform distribution. In this case, the SNR is approximately