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Test: Probability And Expected Value By Mathematical Expectation- 1

40 Questions MCQ Test Business Mathematics and Logical Reasoning & Statistics | Test: Probability And Expected Value By Mathematical Expectation- 1

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This mock test of Test: Probability And Expected Value By Mathematical Expectation- 1 for CA Foundation helps you for every CA Foundation entrance exam. This contains 40 Multiple Choice Questions for CA Foundation Test: Probability And Expected Value By Mathematical Expectation- 1 (mcq) to study with solutions a complete question bank. The solved questions answers in this Test: Probability And Expected Value By Mathematical Expectation- 1 quiz give you a good mix of easy questions and tough questions. CA Foundation students definitely take this Test: Probability And Expected Value By Mathematical Expectation- 1 exercise for a better result in the exam. You can find other Test: Probability And Expected Value By Mathematical Expectation- 1 extra questions, long questions & short questions for CA Foundation on EduRev as well by searching above.

QUESTION: 1

Initially, probability was a branch of

A.
Physics
B.
Statistics
C.
Mathematics
D.
Economics.

Solution:

QUESTION: 2

Two broad divisions of probability are

A.
Subjective probability and objective probability
B.
Deductive probability and non–deductive probability
C.
Statistical probability and Mathematical probability
D.
None of these.

Solution:

QUESTION: 3

Subjective probability may be used in

A.
Mathematics
B.
Statistics
C.
Management
D.
Accountancy

Solution:

QUESTION: 4

An experiment is known to be random if the results of the experiment

A.
Can not be predicted
B.
Can be predicted
C.
Can be split into further experiments
D.
Can be selected at random

Solution:

QUESTION: 5

An event that can be split into further events is known as

A.
Complex event
B.
Mixed event
C.
Simple event
D.
Composite event

Solution:

QUESTION: 6

Which of the following pairs of events are mutually exclusive?

A.
A : The student reads in a school. B : He studies Philosophy.
B.
A : Raju was born in India. B : He is a fine Engineer.
C.
A : Ruma is 16 years old. B : She is a good singer.
D.
A : Peter is under 15 years of age. B : Peter is a voter of Kolkata.

Solution:

QUESTION: 7

If P(A) = P(B), then

A.
A and B are the same events
B.
A and B must be same events
C.
A and B may be different events
D.
A and B are mutually exclusive events

Solution:

QUESTION: 8

If P(A ∩ B) = 0, then the two events A and B are

A.
Mutually exclusive
B.
Exhaustive
C.
Equally likely
D.
Independent.

Solution:

QUESTION: 9

If for two events A and B, P(AUB) = 1, then A and B are

A.
Mutually exclusive events
B.
Equally likely events
C.
Exhaustive events
D.
Dependent events

Solution:

QUESTION: 10

If an unbiased coin is tossed once, then the two events Head and Tail are

A.
Mutually exclusive
B.
Exhaustive
C.
Equally likely
D.
All these (a), (b) and (c)

Solution:

QUESTION: 11

If P(A) = P(B), then the two events A and B are

A.
Independent
B.
Dependent
C.
Equally likely
D.
Both (a) and (c)

Solution:

QUESTION: 12

If for two events A and B, P(A ∩ B) ≠ P(A) × P(B), then the two events A and B are

A.
Independent
B.
Dependent
C.
Not equally likely
D.
Not exhaustiv

Solution:

QUESTION: 13

If P(A/B) = P(A), then

A.
A is independent of B
B.
B is independent of A
C.
B is dependent of A
D.
Both (a) and (b)

Solution:

QUESTION: 14

If two events A and B are independent, then

A.
A and the complement of B are independent
B.
B and the complement of A are independent
C.
Complements of A and B are independent
D.
All of these (a), (b) and (c)

Solution:

QUESTION: 15

If two events A and B are independent, then

A.
They can be mutually exclusive
B.
They can not be mutually exclusive
C.
They can not be exhaustive
D.
Both (b) and (c)

Solution:

QUESTION: 16

If two events A and B are mutually exclusive, then

A.
They are always independent
B.
They may be independent
C.
They can not be independent
D.
They can not be equally likely

Solution:

QUESTION: 17

If a coin is tossed twice, then the events 'occurrence of one head', 'occurrence of 2 heads' and 'occurrence of no head' are

A.
Independent
B.
Equally likely
C.
Not equally likely
D.
Both (a) and (b)

Solution:

QUESTION: 18

The probability of an event can assume any value between

A.
– 1 and 1
B.
0 and 1
C.
– 1 and 0
D.
none of these

Solution:

QUESTION: 19

If P(A) = 0, then the event A

A.
will never happen
B.
will always happen
C.
may happen
D.
may not happen

Solution:

QUESTION: 20

If P(A) = 1, then the event A is known as

A.
symmetric event
B.
dependent event
C.
improbable event
D.
sure event

Solution:

QUESTION: 21

If p : q are the odds in favour of an event, then the probability of that event is

A.
p/q
B.
p/p+q
C.
q/p+q
D.
none of these

Solution:

QUESTION: 22

If P(A) = 5/9, then the odds against the event A is

A.
5 : 9
B.
5 : 4
C.
4 : 5
D.
5 : 14

Solution:

QUESTION: 23

If A, B and C are mutually exclusive and exhaustive events, then P(A) + P(B) + P(C) equals to

A.
1/3
B.
1
C.
0
D.
any value between 0 and 1

Solution:

QUESTION: 24

If A denotes that a student reads in a school and B denotes that he plays cricket, then

A.
P(A ∩ B) = 1
B.
P(A ∪ B) = 1
C.
P(A ∩ B) = 0
D.
P(A) = P(B)

Solution:

QUESTION: 25

P(B/A) is defined only when

A.
A is a sure event
B.
B is a sure event
C.
A is not an impossible event
D.
B is an impossible event

Solution:

QUESTION: 26

P(A/B') is defined only when

A.
B is not a sure event
B.
B is a sure event
C.
B is an impossible event
D.
B is not an impossible event

Solution:

QUESTION: 27

For two events A and B, P(A ∪ B) = P(A) + P(A) only when

A.
A and B are equally likely events
B.
A and B are exhaustive events
C.
A and B are mutually independent
D.
A and B are mutually exclusive

Solution:

QUESTION: 28

Addition Theorem of Probability states that for any two events A and B,

A.
P(A ∪ B) = P(A) + P(B)
B.
P(A ∪ B) = P(A) + P(B) + P(A ∩ B)
C.
P(A ∪ B) = P(A) + P(B) – P(A ∩ B)
D.
P(A ∪ B) = P(A) × P(B)

Solution:

QUESTION: 29

For any two events A and B,

A.
P(A) + P(B) > P(A ∩ B)
B.
P(A) + P(B) < P(A ∩ B)
C.
P(A) + P(B) ≥ P(A∩ B)
D.
P(A) + P(B) ≤ P(A∩ B)

Solution:

QUESTION: 30

For any two events A and B,

A.
P(A–B) = P(A) – P(B)
B.
P(A–B) = P(A) – P(A ∩ B)
C.
P(A–B) = P(B) – P(A ∩ B)
D.
P(B–A) = P(B) + P(A ∩ B)

Solution:

QUESTION: 31

The limitations of the classical definition of probability

A.
it is applicable when the total number of elementary events is finite
B.
it is applicable if the elementary events are equally likely
C.
it is applicable if the elementary events are mutually independent
D.
(a) and (b)

Solution:

QUESTION: 32

According to the statistical definition of probability, the probability of an event A is the

A.
limiting value of the ratio of the no. of times the event A occurs to the number of times the experiment is repeated
B.
the ratio of the frequency of the occurrences of A to the total frequency
C.
the ratio of the frequency of the occurrences of A to the non-occurrence of A
D.
the ratio of the favourable elementary events to A to the total number of elementary events

Solution:

QUESTION: 33

The Theorem of Compound Probability states that for any two events A and B.

A.
P(A ∩ B) = P(A) × P(B/A)
B.
P(A ∪ B) = P(A) × P(B/A)
C.
P(A ∪ B) = P(B) + P(B) – P(A ∩ B)
D.
P(A ∩ B) = P(A) × P(B)

Solution:

Correct Answer :- C

Explanation : If two events, A and B, are mutually exclusive, then the probability that either A or B occurs is the sum of their probabilities.

For mutually inclusive events, P (A or B) = P(A) + P(B) - P(A and B).

QUESTION: 34

If A and B are mutually exclusive events, then

A.
P(A) = P(A–B).
B.
P(B) = P(A–B).
C.
P(A) = P(A ∩ B).
D.
P(B) = P(A ∩ B)

Solution:

QUESTION: 35

If P(A–B) = P(B–A), then the two events A and B satisfy the condition

A.
P(A) = P(B).
B.
P(A) + P(B) = 1
C.
P(A ∩ B) = 0
D.
P(A ∪ B) = 1

Solution:

QUESTION: 36

The number of conditions to be satisfied by three events A, B and C for independence is

A.
2
B.
3
C.
4
D.
any number

Solution:

QUESTION: 37

If two events A and B are independent, then P(A ∩ B)

A.
equals to P(A) + P(B)
B.
equals to P(A) × P(B)
C.
equals to P(A) × P(B/A)
D.
equals to P(B) × P(A/B)

Solution:

QUESTION: 38

Values of a random variable are

A.
always positive numbers.
B.
always positive real numbers.
C.
real numbers.
D.
natural numbers

Solution:

QUESTION: 39

Expected value of a random variable

A.
is always positive
B.
may be positive or negative
C.
may be positive or negative or zero
D.
can never be zero

Solution:

QUESTION: 40

If all the values taken by a random variable are equal then

A.
its expected value is zero
B.
its standard deviation is zero
C.
its standard deviation is positive
D.
its standard deviation is a real number

Solution:

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Test: Probability And Expected Value By Mathematical Expectation- 1

40 Questions MCQ Test Business Mathematics and Logical Reasoning & Statistics | Test: Probability And Expected Value By Mathematical Expectation- 1

Initially, probability was a branch of

Two broad divisions of probability are

Subjective probability may be used in

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