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If j, an Unbiased Estimator of j, Is

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If If   <sub>j</sub>, an unbiased estimator of   <sub>j</sub>, is also a consistent estimator of   <sub>j</sub>, then when the sample size tends to infinity: A) the distribution of   <sub>j</sub> collapses to a single value of zero. B) the distribution of   <sub>j</sub> diverges away from a single value of zero. C)  the distribution of   <sub>j</sub> collapses to the single point   <sub>j</sub>. D)  the distribution of   <sub>j</sub> diverges away from   <sub>j</sub>. j, an unbiased estimator of If   <sub>j</sub>, an unbiased estimator of   <sub>j</sub>, is also a consistent estimator of   <sub>j</sub>, then when the sample size tends to infinity: A) the distribution of   <sub>j</sub> collapses to a single value of zero. B) the distribution of   <sub>j</sub> diverges away from a single value of zero. C)  the distribution of   <sub>j</sub> collapses to the single point   <sub>j</sub>. D)  the distribution of   <sub>j</sub> diverges away from   <sub>j</sub>. j, is also a consistent estimator of If   <sub>j</sub>, an unbiased estimator of   <sub>j</sub>, is also a consistent estimator of   <sub>j</sub>, then when the sample size tends to infinity: A) the distribution of   <sub>j</sub> collapses to a single value of zero. B) the distribution of   <sub>j</sub> diverges away from a single value of zero. C)  the distribution of   <sub>j</sub> collapses to the single point   <sub>j</sub>. D)  the distribution of   <sub>j</sub> diverges away from   <sub>j</sub>. j, then when the sample size tends to infinity:


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