Unequal N HSD. This post hoc test can be used to determine the significant differences between group means in an analysis of variance setting. The Unequal N HSD test is a modification of the Tukey HSD test, and it provides a reasonable test of differences in group means if group n's are not too discrepant (for a detailed discussion of different post hoc tests, see Winer, Michels, & Brown (1991). For more details, see General Linear Models. See also, Post Hoc Comparisons. For a discussion of statistical significance, see Elementary Concepts.

Uniform Distribution. The discrete Uniform distribution (the term first used by Uspensky, 1937) has density function:

f(x) = 1/N          x = 1, 2, ..., N

The continuous Uniform distribution has density function: where
 

f(x) = 1/(b-a)        a < x < b

a     is the lower limit of the interval from which points will be selected
b     is the upper limit of the interval from which points will be selected

Unimodal Distribution. A distribution that has only one mode. A typical example is the normal distribution which happens to be also symmetrical but many unimodal distributions are not symmetrical (e.g., typically the distribution of income is not symmetrical but "left-skewed"; see skewness). See also bimodal distribution, multimodal distribution.

Unit Penalty. In several search algorithms, a penalty factor which is multiplied by the number of units in the network and added to the error of the network, when comparing the performance of the network with others. This has the effect of selecting smaller networks at the expense of larger ones. See also, Penalty Function.

Unit Types (in Neural Networks). Units in the input layer are extremely simple: they simply hold an output value, which they pass onto units in the second layer. Input units do no processing. Input units have their synaptic function set to Dot Product, and their activation function set to Identity by default; actually these functions are ignored in input units.

Each hidden or output unit has a number of incoming connections from units in the preceding layer (the fan-in): one for each unit in the preceding layer. Each unit also has a threshold value.

The outputs of the units in the preceding layer, the weights on the associated connections, and the threshold value are fed through the unit's synaptic function (post synaptic potential function) to produce a single value (the unit's input value).

The input value is passed through the unit's activation function to produce a single output value, also known as the activation level of the unit.

Unsupervised Learning in Neural Networks. Training algorithms that adjust the weights in a neural network by reference to a training data set including input variables only. Unsupervised learning algorithms attempt to locate clusters in the input data.

Unweighted Means. If the cell frequencies in a multi-factor ANOVA design are unequal, then the unweighted means (for levels of a factor) are calculated from the means of sub-groups without weighting, that is, without adjusting for the differences between the sub-group frequencies.