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  2. Semi-log plot - Wikipedia

    en.wikipedia.org/wiki/Semi-log_plot

    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 lines are: y = 10 x (red), y = x (green), y = log ( x ) (blue). In science and engineering, a semi-log plot / graph or semi-logarithmic plot / graph has one axis on a logarithmic scale, the other on a linear scale.

  3. Logarithm - Wikipedia

    en.wikipedia.org/wiki/Logarithm

    Because log(x) is the sum of the terms of the form log(1 + 2 −k) corresponding to those k for which the factor 1 + 2 −k was included in the product P, log(x) may be computed by simple addition, using a table of log(1 + 2 −k) for all k. Any base may be used for the logarithm table. Applications A nautilus shell displaying a logarithmic spiral

  4. Work sampling - Wikipedia

    en.wikipedia.org/wiki/Work_sampling

    Work sampling is the statistical technique used for determining the proportion of time spent by workers in various defined categories of activity (e.g. setting up a machine, assembling two parts, idle…etc.). [1] It is as important as all other statistical techniques because it permits quick analysis, recognition, and enhancement of job ...

  5. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    An explanation of logistic regression can begin with an explanation of the standard logistic function. The logistic function is a sigmoid function, which takes any real input , and outputs a value between zero and one. [2] For the logit, this is interpreted as taking input log-odds and having output probability.

  6. Log-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Log-normal_distribution

    Log-normal distribution. 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.

  7. Empirical distribution function - Wikipedia

    en.wikipedia.org/wiki/Empirical_distribution...

    The empirical distribution function is an estimate of the cumulative distribution function that generated the points in the sample. It converges with probability 1 to that underlying distribution, according to the Glivenko–Cantelli theorem. A number of results exist to quantify the rate of convergence of the empirical distribution function to ...

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