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Median absolute deviation (MAD): It is the very robust estimator of the scale given by the following equation
or, in other words we can say that, the median of the absolute deviations from the median of data. In order to use MAD as the consistent estimator of the standard deviation it is multiplied by a scale factor which depends on the distribution of the data. For normally distributed data the constant is 1.4826 and expected value of 1.4826 MAD is approximately equal to population standard deviation.
an oil company is considering whether or not to bid for an offshore drilling contract. The bid would cost $60 with a 65% chance of gaining the contract. Outcome success Probability
Invariant transformations to combine marginal probability functions to form multivariate distributions motivated by the need to enlarge the class of multivariate distributions beyo
In a mathematics examination the average grade was 82 and the standard deviation was 5. all students with grade from 88 to 94 received grade of B. if the grade are approximately no
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if Q = ESS/2 >
Quincunx is the device used by Galton to illustrate his lectures, which is shown in the Figure. It had a glass face and a funnel at its top. The shot was passed through funnel an
The particular projection which an investigator believes is most likely to give an accurate prediction of the future value of some process. Commonly used in the context of the anal
L'Abbe ´ plot is often used in the meta-analysis of the clinical trials where the result is the binary response of it. The event risk (number of events/number of the patients in a
O. J. Simpson paradox is a term coming from the claim made by the defence lawyer in murder trial of O. J. Simpson. The lawyer acknowledged that the statistics demonstrate that onl
Over dispersion is the phenomenon which occurs when empirical variance in the data exceeds the nominal variance under some supposed model. Most often encountered when the modeling
This is extension of the EM algorithm which typically converges more slowly than EM in terms of the iterations but can be much faster in the whole computer time. The general idea o
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