Question # 1

The main advantages of using the range as a measure of dispersion is that.

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It is easy to calculate

It is heavily influenced by extreme values.

It can change drastically from one data set to the next

It is determined by only two points in the data set

Variance σ²is equal to E(y²) - --------------------

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E(y)

[E(y)]²

E(y)²

E² (y)

If x is a continuous random vriable, then the function f (x) is.

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A probability function

A probability denaity function

A density function

Both b and c

Quartile Co-efficient of skewness is also called as

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Median co-efficent of skewness

Pearson's 1st co-efficient of skewness

Pearson's 2nd co-efficient of skewness

None of these

the term 'event' is used for.

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Time

Subaet of the sample space

Total number of outcomes

Probability

An index having a wide scope is called:

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Price index number

General purpose index number

Special purpose index number

None of these

The hypergeometric distribution is used when trials are

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Dependent

Independent

Equally likely

Mutually exclusive

Continuous data are differentitated from discrete data in that

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Discrete data classes are represnted by fractions

Continuous data classes may be represented by fractions

Continous data take on only whole numebrs

Discrete data can take on any real number

The mean deviation of dispersion can be negative.

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Often

Sometimes

Always

Never

If n is the number of elements of a set. the total numebr of subsects of this set in

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2n

n2

2ⁿ

n

Variance of binomial distribution is

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np

√npq

npq

nq

Which index number has a wide soope

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Special

General

Price

Quantity

The whole issue of descriptive and inferential statistic can be described with the help of.

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Statistical analysis

Statistic

Statistical problem

Statistical steps

When all the values in a sereis occur the same numebr of times, then it in not possible to compute the.

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Mean

Median

Mode

Weighted mean

If the value of a variable are -2, -3, -5, -10 then range is.

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-12

8

-8

0

In binomial distribution it is impossible to find

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P(x L)

P(x=0)

P(x > 0)

P(0≤ x≤∞)

In a symmetrical distribution the coefficient of skewness is equal to.

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-1

+1

0

None of these

If C is a non -random variabe than E (C) is.

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c

0

1

x

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