Search results
Results from the WOW.Com Content Network
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.
In statistics and applications of statistics, normalization can have a range of meanings. [1] In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging.
Raw data is a relative term (see data), because even once raw data have been "cleaned" and processed by one team of researchers, another team may consider these processed data to be "raw data" for another stage of research. Raw data can be inputted to a computer program or used in manual procedures such as analyzing statistics from a survey.
Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines , logistic regression , and artificial neural networks ).
The upper plot uses raw data. In the lower plot, both the area and population data have been transformed using the logarithm function. In statistics , data transformation is the application of a deterministic mathematical function to each point in a data set—that is, each data point z i is replaced with the transformed value y i = f ( z i ...
In statistics, quantile normalization is a technique for making two distributions identical in statistical properties. To quantile-normalize a test distribution to a reference distribution of the same length, sort the test distribution and sort the reference distribution.
Comparison of the various grading methods in a normal distribution, including: standard deviations, cumulative percentages, percentile equivalents, z-scores, T-scores. In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured.
The Softmax function is a smooth approximation to the arg max function: the function whose value is the index of a vector's largest element. The name "softmax" may be misleading.