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Expectancy–value theory has been developed in many different fields including education, health, communications, marketing and economics. Although the model differs in its meaning and implications for each field, the general idea is that there are expectations as well as values or beliefs that affect subsequent behavior.
Any definition of expected value may be extended to define an expected value of a multidimensional random variable, i.e. a random vector X. It is defined component by component, as E[X] i = E[X i]. Similarly, one may define the expected value of a random matrix X with components X ij by E[X] ij = E[X ij].
The expectancy theory of motivation explains the behavioral process of why individuals choose one behavioral option over the other. This theory explains that individuals can be motivated towards goals if they believe that there is a positive correlation between efforts and performance, the outcome of a favorable performance will result in a desirable reward, a reward from a performance will ...
An expectation about the behavior or performance of another person, expressed to that person, may have the nature of a strong request, or an order; this kind of expectation is called a social norm. The degree to which something is expected to be true can be expressed using fuzzy logic. Anticipation is the emotion corresponding to expectation.
In this case, the structure of the real numbers makes it possible to define quantities such as the expected value and variance of a random variable, its cumulative distribution function, and the moments of its distribution. However, the definition above is valid for any measurable space of values.
A large majority of people prefer the sure thing over the gamble, although the gamble has higher (mathematical) expected value (also known as expectation). The expected value of a monetary gamble is a weighted average, in which each possible outcome is weighted by its probability of occurrence. The expected value of the gamble in this example ...
The expected value or mean of a random vector is a fixed vector [] whose elements are the expected values of the respective random variables. [ 3 ] : p.333 E [ X ] = ( E [ X 1 ] , . . .
Second, it is also used to abbreviate "expect value", which is the expected number of times that one expects to obtain a test statistic at least as extreme as the one that was actually observed if one assumes that the null hypothesis is true. [49] This expect-value is the product of the number of tests and the p-value.