In Linear Arrangement, people will be arranged or seated in one or multiple rows. Answer: (4) EQ 3. Neither I nor II is sufficientE. With the demise of sola scriptura sufficiency cannot stand. We can also very easily final the final answer from Statement II. .
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To see this, consider the joint probability density function of
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a maximum likelihood estimate).
More generally, the “unknown parameter” may represent a vector of unknown quantities or may represent everything about the model that is unknown or not fully specified.
,
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, Xn). I alone is sufficient while II alone is not sufficientB. Both the statistic and the underlying parameter can be vectors. Analyse and answer whether the data provided in the two statements are sufficient to answer the question or not. 5 In other words, the data processing inequality becomes an equality:
As an example, the sample mean is sufficient for the mean (μ) of a normal distribution with known variance. .
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From II, we conclude find out 31st March, 2006 was Friday. Never assume anything by yourself. . Thus the density takes form required by the Fisher–Neyman factorization theorem, where h(x)=1{min{xi}≥0}, and the rest of the expression is a function of only θ and T(x)=max{xi}. II alone is sufficient while I alone is not sufficientC.
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