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Probability distribution: For the discrete random variable, a mathematical formula which provides the probability of each value of variable. See, for instance, binomial distribution and Poisson distribution. For the continuous random variable, a curve described by the mathematical formula which specifies, by way of area under the curve, the probability which the variable falls within the particular interval. Examples comprise the normal distribution and the exponential distribution. In both the cases the term probability density might also be used. (A distinction is sometimes made among 'density' and 'distribution', when the latter is reserved for probability that the random variable comes below some value. In this dictionary, though, the latter will be termed the cumulative probability distribution and the probability distribution and probability density both are used synonymously.
CONSTRUCTION OF AN OR MODEL
Genetic algorithms: The optimization events motivated by the biological analogies. The prime idea is to try to mimic the 'survival of the fittest' rule of the genetic mutation in
The biggest and smallest variate values among the sample of observations. Significant in various regions, for instance flood levels of the river, speed of wind and snowfall.
Matching coefficient is a similarity coefficient for data consisting of the number of binary variables which is often used in cluster analysis. It can be given as follows he
Kendall's tau statistics : The measures of the correlation between the two sets of rankings. Kendall's tau itself (τ) is the rank correlation coefficient based on number of inversi
The transformation of the Pearson's product moment correlation coefficient, r, can be given by The statistic z has the normal distribution with mean here ρ is the pop
Lancaster models : The means of representing the joint distribution of the set of variables in terms of the marginal distributions, supposing all the interactions higher than a par
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
cholscores Treatment income ($000) Patient ID low Income? 0.6 Old 21.3 2 Yes 0.17 Old 27.2 13 Yes 0.69 New 27.1 16 Yes 1.09 Old 94.8
Confounding: A procedure observed in some factorial designs in which it is impossible to differentiate between some main effects or interactions, on the basis of the particular d
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