# 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

MCQ▾

### 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

MCQ▾

### 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

MCQ▾

### 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

MCQ▾

### Question

Which of the following definitions of Sufficiency of a estimator is known as Fisher-Neyman 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

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