Statistical Inference and Hypothesis Testing (ISS (Statistical Services) Statistics Paper II (New 2016 MCQ Pattern)): Questions 136  139 of 222
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Question number: 136
» Statistical Inference and Hypothesis Testing » Confidence Interval Estimation
Question
The health organization wants to study the mean weight of Indian women of the age group 30  40. A sample of forty woman of that age group is randomly selected and their weights in pounds are recorded.
140  153  167  115 
136  136  145  125 
147  134  154  148 
136  126  139  132 
152  132  139  120 
130  138  138  146 
116  137  143  115 
159  147  122  145 
128  123  134  127 
131  154  150  116 
The calculated standard deviation is σ = 12.77. Then the 99 % confidence interval for the mean weight is
Choices
Choice (4)  Response  

a. 


b. 


c. 


d. 


Question number: 137
» Statistical Inference and Hypothesis Testing » Moments and Least Squares
Question
By the method of moments one can estimate
Choices
Choice (4)  Response  

a.  All Moments of a population distribution 

b.  All constants of a population 

c.  Only mean and variance of a distribution 

d.  None of the above 

Question number: 138
» Statistical Inference and Hypothesis Testing » Confidence Interval Estimation
Question
Consider the general form of confidence interval for the mean .
In this expression is known as ………. and is known as ……………….
Choices
Choice (4)  Response  

a.  Critical value, Standard Error 

b.  Point estimate, Standard deviation 

c.  Control limit, variance 

d.  All of the above 

Question number: 139
» Statistical Inference and Hypothesis Testing » Factorization Theorem
Question
Which of the following definitions of Sufficiency of a estimator is known as FisherNeyman Factorization Theorem?
Choices
Choice (4)  Response  

a.  An estimator is said to be sufficient for if it provides all the information contained in the sample about the parametric function 

b.  If is a estimator of parameter based on a sample of size n from the population with density such that the conditional distribution of given T is independent of then T is sufficient estimator of . 

c.  A statistic is sufficient estimator of parameter if and only if the likelihood function (joint p. d. f. of the sample) can be expressed as where is the p. d. f. of statistic and is a function of sample observations only independent of 

d.  All of the above 
