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The syntax of the IIf function is as follows: IIf(expr, truepart, falsepart) All three parameters are required: e expr is the expression that is to be evaluated. truepart defines what the IIf function returns if the evaluation of expr returns true. falsepart defines what the IIf function returns if the evaluation of expr returns false.
A more general definition of conditional mutual information, applicable to random variables with continuous or other arbitrary distributions, will depend on the concept of regular conditional probability. [4] Let (,,) be a probability space, and let the random variables , , and each be defined as a Borel-measurable function from to some state ...
In words: the variance of Y is the sum of the expected conditional variance of Y given X and the variance of the conditional expectation of Y given X. The first term captures the variation left after "using X to predict Y", while the second term captures the variation due to the mean of the prediction of Y due to the randomness of X.
The image of a function f(x 1, x 2, …, x n) is the set of all values of f when the n-tuple (x 1, x 2, …, x n) runs in the whole domain of f.For a continuous (see below for a definition) real-valued function which has a connected domain, the image is either an interval or a single value.
The joint distribution encodes the marginal distributions, i.e. the distributions of each of the individual random variables and the conditional probability distributions, which deal with how the outputs of one random variable are distributed when given information on the outputs of the other random variable(s).
In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value evaluated with respect to the conditional probability distribution. If the random variable can take on only a finite number of values, the "conditions" are that the variable can only take on a subset of ...
Let X and Y be random variables taking real values, and let Z be the n-dimensional vector-valued random variable. Let x i, y i and z i denote the ith of i.i.d. observations from some joint probability distribution over real random variables X, Y, and Z, with z i having been augmented with a 1 to allow for a constant term in the regression.
When using multinomial logistic regression, one category of the dependent variable is chosen as the reference category. Separate odds ratios are determined for all independent variables for each category of the dependent variable with the exception of the reference category, which is omitted from the analysis. The exponential beta coefficient ...