Target variable

The expected change in the dependent (= target) variable (effect) should be sufficient to distinguish the new point from the old ones. 

This is the mutual information between the target variable (range and Doppler) X and the processed (with a matched filter) radar return Y, resulting from the use of the waveform identity matrix. We use this expected information as the MoE of the waveform more information we extract from the situation the better. 

The index t in the above equation corresponds to the training instance, X is the vector of input variables, Y is the vedor of target variables, q is a learning rate,... 

If interpolation of the values of the target variable y is necessary, for example to find better estimates of the model error (for tests of adequacy), the variables, z, in Eq. 3-9 must be retransformed using Eqs. 3-6 to 3-8. 

In practice, the throughput of a machine is generally predefined, while the energy input and pressure generation are target variables for the calculation. It is therefore sensible to formulate the dimensionless power and the dimensionless pressure generation as functions of their dimensionless influencing variables. 

Software sensors are virtual sensors which calculate the desired variable or parameter from related physical measurements [58]. In other words, there must always be a model available that relates reliably the measured variable with the target variable or parameter. Normally, measured variables are easily measurable effects that are caused and influenced by the target. The most prominent software sensor is the respiratory quotient (RQ-value) which characterizes the physiological state of a culture. However, its determination can be tricky (see Sect. 5). 

Many pitfalls of dimensional analysis are associated with the selection of the reference list, target variable, or measurement errors (e.g., when friction losses are of the same order of magnitude as the power consumption of the motor). The larger the scale-up factor, the more precise the measurements of the smaller scale have to be. °° ... 

The rows of the matrix consist of the basic dimensions, while the columns represent the physical quantities from the relevance list. The most important physical properties and process-related parameters, as well as the target variable (that is, the one we would like to predict on the basis of other variables) are placed in one of the columns of the residual matrix. 

One target variable (Power consumption) and seven process variables/constants thus represent the number n = 8 of the If-theorem. The number of basic dimensions r = 3 (M, L, and T). According to the theorem, the process can be reduced to the relationship between m = n — r=8 — 3 = 5 mutually independent dimensionless groups. 

It is desired to run a placebo-controlled parallel group trial in asthma. The target variable is forced expiratory volume in one second (FEVi). The clinically relevant difference is presumed to be 200 ml and the standard deviation 450 ml. A two-sided significance level of 0.05 (or 5%) is to be used and the power should be 0.8 (or 80%). What should the sample size be. ... 

Such heterotrophic data are suited to derive predictive models using simple covariance function models (Wackernagel, 2003). Assuming that successful and robust functional relationships are derived, models can be used to predict a target variable (e.g., level 111 indicator variable) at unsampled locations across a wetland(s). The prediction range should match the model range to avoid extrapolations with high uncertainties. 

CK) is the multivariate extension of kriging that combines a sparsely measured primary variable (or target variable) with a denser set of ancillary data as secondary variable (e.g., remote sensing... 

Most of the strategies devoted to managing risk in projects at the design stage target variability. One very popular tool is known as six-sigma (Pande and Holpp, 2001). Companies also make use of failure mode effects analysis (Stamatis, 2003), which is a procedure originated at NASA in which potential failures are analyzed and measures to... 

By assigning a softmax activation function on the output layer of the neural network (or a softmax component in a component-based neural network) for categorical target variables, the outputs can be interpreted as posterior probabilities. This is very useful in classification as it gives a certainty measure on classifications. 

These are needed to characterize temporal and spatio-temporal behaviours of the system. For this, it is desirable to distinguish between dependent and independent variables. Furthermore, target variables which play a key role have to identified along with non-target variables which play a secondary role [5]. To illustrate the point, let us examine the dependence of quantity 2, demanded of particular commodity Z which would depend on P, price of commodity Z, Pq price of other commodities, Y income of the consumer and T the suitable measure of the taste. If we take a broader view, 02 would also depend on the following If... 

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