# ISS (Statistical Services) Statistics Paper I (Old Subjective Pattern): Questions 36 - 42 of 165

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## Question number: 36

» Statistical Methods » Non-Parametric Test » Run

Appeared in Year: 2012

### Describe in Detail

Name two non-parametric tests for comparing locations of two correlated populations.

### Explanation

The Wald-Wolfowitz runs test and Mann-Whitney U-test comparing locations of two correlated populations. Let the sample 1 consider the population X and sample 2 consider the population Y. The hypothesis whether two sample comes from same identical population. We test the following null and alternative hypothesis is

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## Question number: 37

» Statistical Methods » Non-Parametric Test » Wald-Wolfowitz

Appeared in Year: 2013

### Describe in Detail

The values of two random samples are arranged below in an increasing order with X denoting the value of sample I and Y denoting the value of sample 2:

XXXYXYYYYXXYXXXYXXYYY

Use Wald-Wolfowitz run test to test whether the two samples may be regarded as coming from a common population.

### Explanation

Wald-Wolfowitz run test uses the data of two random samples, sample 1 is X variables of size n = 11 and sample 2 is Y variables of size m = 10 from two population F _{X} (x) and F _{Y} (y) respectively.

The hypothesis under the test is that the null hypothesis is the two population are identical otherwise the alternative is the population is differ.

H _{0}: F

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## Question number: 38

» Probability » Probability Generating Functions

Appeared in Year: 2009

### Describe in Detail

Given the distribution function

find its probability density function.

### Explanation

The distribution function written in this form

The density function is

where

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## Question number: 39

» Probability » Convergence » In Distribution

Appeared in Year: 2013

### Describe in Detail

Show that convergence in probability implies convergence in distribution.

### Explanation

The sequence X _{n} converges to X in probability if for any ε > 0

The sequence X _{n} converges to the distribution of X as n tends to infinity if

For ε > 0,

Hence

……… (1)

Note that

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## Question number: 40

» Probability » Probability of M Events Out of N

Appeared in Year: 2009

### Describe in Detail

A die is rolled twice. Let A, B, C denote the events respectively that the sum of scores is 6, the sum of scores is 7, and the first score is 4. Are A and C independent? Are B and C independent?

### Explanation

A die is rolled twice, the sample space consists of thirty six outcomes. The sample space is

(1,1), (1,2), (1,3), (1,4), (1,5), (1,6)

(2,1), (2,2), (2,3), (2,4), (2,5), (2,6)

(3,1), (3,2), (3,3), (3,4), (3,5), (3,6)

(4,1), (4,2), (4,3), (4,4), (4,5), (4,6)

(5,1), (5,2), (5,3), (5,4), (5,5), (5,6)

(6,1), (6,2), (6,3), (6,4), (5,5), (5,6)

Total outcomes (n)

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## Question number: 41

» Numerical Analysis » Interpolation Formulae » Lagrange

Appeared in Year: 2013

### Describe in Detail

Find the missing value in the table by assuming a polynomial form for y:

X | 0 | 1 | 2 | 3 | 4 |

y | 1 | 2 | 4 | - | 16 |

### Explanation

we have given that there are four values of y is given, then find the missing value of y at x = 3 using Lagrange’s interpolation polynomial because the x values is not equal interval.

The table is

X | 0 | 1 | 2 | 4 |

y | 1 | 2 | 4 | 16 |

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## Question number: 42

» Probability » Tchebycheffs Inequality

Appeared in Year: 2013

### Describe in Detail

Let X be a random variable with E [X] = 4 and E [X ^{2}] = 20. Use Chebyshev’s inequality to determine a lower bound for the probability P [0 < x < 8].

### Explanation

Let X be a random variable with mean µ and variance σ ^{2}. Then any k > 0, the Chebyshev’s inequality is

or

σ ^{2} = E [X ^{2}] - (E [X] ) ^{2} =4

Then, a lower bound for the probability

Using Chebyshev’s inequality

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