A. It is a function of some other set of sufficient statistics.
B. It is a function of every other set of sufficient
C. It is a function of any sufficient statistics in the set.
D. It is not a function of every other set of sufficient statistics.
A. It is a function of some other set of sufficient statistics.
B. It is a function of every other set of sufficient
C. It is a function of any sufficient statistics in the set.
D. It is not a function of every other set of sufficient statistics.
A. MSE(θˆ)=SD(θˆ)+Bias
B. MSE(θˆ)=Var(θˆ)+Bias2
C. MSE(θˆ)=SD(θˆ)+Bias2
D. MSE(θˆ)=Var(θˆ)+Bias
A. Independence Equation
B. Sequential Probability Likelihood Equation
C. Neyman Pearson Lemma
D. Wald’s Equation
A. Minimal sufficient statistic
B. Sufficient statistic
C. Efficient
D. Minimax statistics
A. Sufficient
B. Efficient
C. Unbiased
D. Consistent
A. Consistent
B. Sufficient
C. Efficient
D. Unbiased
A. Kruskal-Wallis and Wilcoxon tests
B. Runs test
C. Chi-Square and sign test
D. F test
A. Lower tailed
B. Upper tailed
C. Either of the above
D. None of the above
A. We count them
B. We discard them
C. We depends upon the scores
D. non of these
A. Ties within one sample may affect the decision
B. Ties always affect the decision
C. Ties never affect the decision
D. Ties between the two sample may affect the decision