Search results
Results from the WOW.Com Content Network
Given a discrete random variable , which may be any member within the set and is distributed according to : [,], the entropy is ():= (), where denotes the sum over the variable's possible values. [ Note 1 ] The choice of base for log {\displaystyle \log } , the logarithm , varies for different applications.
This is a measure of how much information can be obtained about one random variable by observing another. The mutual information of relative to (which represents conceptually the average amount of information about that can be gained by observing ) is given by:
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4] The parameters used are:
In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation.Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data.
The Shannon information is closely related to entropy, which is the expected value of the self-information of a random variable, quantifying how surprising the random variable is "on average". This is the average amount of self-information an observer would expect to gain about a random variable when measuring it. [1]
The expected value of a random variable with a finite number of outcomes is a weighted average of all possible outcomes. In the case of a continuum of possible outcomes, the expectation is defined by integration. In the axiomatic foundation for probability provided by measure theory, the expectation is given by Lebesgue integration.
More specifically, it quantifies the "amount of information" (in units such as shannons , nats or hartleys) obtained about one random variable by observing the other random variable. The concept of mutual information is intimately linked to that of entropy of a random variable, a fundamental notion in information theory that quantifies the ...
A variable of this type is called a dummy variable. If the dependent variable is a dummy variable, then logistic regression or probit regression is commonly employed. In the case of regression analysis, a dummy variable can be used to represent subgroups of the sample in a study (e.g. the value 0 corresponding to a constituent of the control ...