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The graphic representation of the alternatives in a decision making problem which summarizes all the possibilities foreseen by the decision maker. For instance, suppose we are given the below written problem.
A physician should choose between two treatments. The patient is known to have one of the two diseases but the diagnosis is not very certain. A thorough examination of the patient was not able to resolve the diagnostic uncertainty. The best which can be said is that the probability which the patient has disease A is p. An easy decision tree for the problem is shown in the figure drawn below.
Chain-binomial models : Models arising in mathematical theory of the quite infectious diseases, which postulate that at any stage in the epidemic there are a certain number of the
moving and semi average method graphical reprsentation
Cointegration : The vector of not motionless time sequence is said to be cointegrated if the linear combination of the individual series is stationary. Facilitates suitable testing
The theory of measurement which recognizes that in any measurement situation there are multiple (actually infinite) sources of variation (known as facets in the theory), and that a
Consider a decision faced by a cattle breeder. The breeder must decide how many cattle he should sell in the market each year and how many he should retain for breeding purposes. S
This is acronym for the Epidemiological, Graphics, Estimation and Testing of the program developed for the analysis of the data from studies in epidemiology. It can be made in use
You have probably noticed by now that some of the statements of necessary and sufficient conditions sound more natural than others. For example it seems more natural to express "We
Outlier is an observation which seems to deviate markedly from the other members of the sample in which it happens. In the set of systolic blood pressures, {125, 128, 130, 131, 19
Multiple imputation : The Monte Carlo technique in which missing values in the data set are replaced by m> 1 simulated versions, where m is usually small (say 3-10). Each of simula
1) Let N1(t) and N2(t) be independent Poisson processes with rates, ?1 and ?2, respectively. Let N (t) = N1(t) + N2(t). a) What is the distribution of the time till the next epoch
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