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Negative hyper geometric distribution: In sampling without replacement from the population comprising of r elements of one kind and N - r of another, if two elements corresponding to which selected are replaced every time, then the probability of finding x elements of first kind in a random sample of n elements can be given as follows
The mean of distribution can be given as Nr/N and the variance can be given as follows (nr/N)(1-r/N)(N+n)/(N+1).
It corresponds to the beta binomial distribution with the integral parameter values.
Probability judgements : Human beings often require assessing the probability which some event will occur and accuracy of these probability judgements often determines success of o
Bubble plot : A method or technique for displaying the observations which involve three variable values. Two of the variables are used to make a scatter diagram and values of the t
methods of determining trend in time series?
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
Common cause failures (CCF): Simultaneous failures of the number of components due to a same reason. A reason can be external to the components, or it can be the single failure wh
This graph for Cross Correlation Function for RES1, RES1 shows that there is possibly negative autocorrelation as there are alternating spikes; also the first spike is negative whi
Duck Lovers Unlimited (DLU) Inc. assembles specially configured light jet aircrafts for airborne duck hunting. The quarterly demand forecasts for the upcoming fiscal year are:
An approach of using the likelihood as the basis of estimation without the requirement to specify a parametric family for data. Empirical likelihood can be viewed as the example of
Auto correlation : The correlation of the internal observations in the time series, generally expressed as a function of the time lag between the observations. It is also used for
Link functions: The link function relates the linear predictor ηi to the expected value of the data. In classical linear models the mean and the linear predictor are identical
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