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From the mean value theorem, there exists a value ξ in the interval between x and y where the derivative f ′ equals the slope of the secant line: (,): ′ = ()The logarithmic mean is obtained as the value of ξ by substituting ln for f and similarly for its corresponding derivative:
A volcano plot is constructed by plotting the negative logarithm of the p value on the y axis (usually base 10). This results in data points with low p values (highly significant) appearing toward the top of the plot. The x axis is the logarithm of the fold change between the two conditions. The logarithm of the fold change is used so that ...
In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed.Thus, if the random variable X is log-normally distributed, then Y = ln(X) has a normal distribution.
Log loss is always greater than or equal to 0, equals 0 only in case of a perfect prediction (i.e., when = and =, or = and =), and approaches infinity as the prediction gets worse (i.e., when = and or = and ), meaning the actual outcome is "more surprising". Since the value of the logistic function is always strictly between zero and one, the ...
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.
In mathematics, the binary logarithm (log 2 n) is the power to which the number 2 must be raised to obtain the value n.That is, for any real number x, = =. For example, the binary logarithm of 1 is 0, the binary logarithm of 2 is 1, the binary logarithm of 4 is 2, and the binary logarithm of 32 is 5.
M is, therefore, the binary logarithm of the intensity ratio (or difference between log intensities) and A is the average log intensity for a dot in the plot. MA plots are then used to visualize intensity-dependent ratio of raw microarray data (microarrays typically show a bias here, with higher A resulting in higher |M|, i.e. the brighter the ...
Usually the mode of a binomial B(n, p) distribution is equal to ⌊ (+) ⌋, where ⌊ ⌋ is the floor function. However, when ( n + 1) p is an integer and p is neither 0 nor 1, then the distribution has two modes: ( n + 1) p and ( n + 1) p − 1 .