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Artificial neural network: A mathematical arrangement modelled on the human neural network and designed to attack various statistical problems, particularly in the region of pattern recognition, multivariate analysis, memory and learning. The significant feature of such a structure is a network of the simple processing elements (such as arti?cial neurons) coupled together (the hardware or software), such that they cooperate. From the set of 'inputs' and an associated set of the parameters, the arti?cial neurons produce an 'output' which provides a possible solution to the problem under the investigation. In number of neural networks the relationship between the input received by the neuron and its output.
The most is determined by the generalized linear model ordinary form is the feed-forward network which is fundamentally an extension of the idea of the perception. In this type of network the vertices can be numbered so that all the connections go from a vertex to one with the one possessing the higher number; the vertices are organised in the form of layers, with connections only to the higher layers. This is illustrated in the figure draw below each neuron sums its inputs to form a whole input
xj and applies the function fj to xj to give the result yj. The links have weights wij which multiply the signals travelling along them by the factor. Number of ideas and activities familiar to statisticians can be expressed in the neural-network notation, consisting regression analysis, generalized additive models, and the discriminant examination. In any practical problem the statistical equivalent of specifying architecture of the suitable network is specifying a appropriate model, and training the network to perform well with the reference to a training set is equivalent to estimating the parameters of the model given as the set of data.
Profile plots is a technique of representing the multivariate data graphically. Each of the observation is represented by a diagram comprising of a sequence of equispaced vertical
Grade of membership model: This is the general distribution free method for the clustering of the multivariate data in which only categorical variables are included. The model ass
Time series : The values of a variable recorded, generally at a regular interval, over the long period of time. The observed movement and fluctuations of several such series are
Negative binomial distribution is the probability distribution of number of failures, X, before the kth success in the sequence of Bernoulli trials where the probability of succes
A term which covers the large number of techniques for the analysis of the multivariate data which have in common the aim to assess whether or not the set of variables distinguish
Interim analyses : An analysis made before the planned end of a clinical trial, typically with the aim of detecting the treatment differences at the early stage and thus preventing
Latin square is an experimental design targeted at removing from the experimental error the variation from two extraneous sources so that a more sensitive test of the treatment ef
The regression analysis is used to fit a model describing the relationship of a dependent variable with independent variable(s). Here we have fitted three regression models:
with the help of regression analysis create a model that best describes the situation. Indicate clearly the effect that each factors given in the attached file and other factors ma
Hazard plotting is based on the hazard function of a distribution, this procedure gives estimates of distribution parameters, the proportion of units failing by the given time per
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