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Zero-inflated Poisson regression is the model for count data with the excess zeros. It supposes that with probability p the only possible observation is 0 and with the probability 1 p a random variable with the Poisson distribution is observed. For instance, when manufacturing equipment is properly aligned, defects might be almost impossible. But when it is misaligned, defects might happen according to a Poisson distribution. Both probability p of the perfect zero defect state and the mean number of defects λ in the imperfect state might depend on covariates. The parameters in this type of models can be estimated using maximum likelihood estimation.
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
Personal probabilities : A radically special approach for allocating probabilities to events than, for instance, the commonly used long-term relative frequency approach. In this ty
Hi , Im currently taking the course Financial Econometrics of Master of Finance at RMIT. I find it really difficult to understand the course''s material and now im having the majo
distinguish the historigram and histogram
Kolmogorov Smirnov two-sample method is a distribution free technique which tests for any difference between the two populations probability distributions. The test is relied on t
Kalman filter : A recursive procedure which gives an estimate of the signal when only the 'noisy signal' can be observed. The estimate is efficiently constructed by putting the exp
An approach to investigations designed to recognize a particular medical condition in the large population, usually by means of a blood test, which might result in the considerable
A radically different approach of dealing with the uncertainty than the traditional probabilistic and the statistical methods. The necessary feature of the fuzzy set is a membershi
MEANING ,IMPORTANCE AND RELEAVANCE OF SCATTER DIAGRAM
Briefly explain the importance of forecasting for managers?
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