SlideShare ist ein Scribd-Unternehmen logo
1 von 41
Downloaden Sie, um offline zu lesen
Probability Lesson 2
www.MathAcademy.sg
Mr Ian Ang
c⃝ 2015 Math Academy www.MathAcademy.sg 1
Venn diagram
Let ℩ denote the entire probability space.
A âˆȘ B represents union =⇒ “Take everything in A and B”.
A ∩ B represents intersection =⇒ “Take common parts in A and B”.
c⃝ 2015 Math Academy www.MathAcademy.sg 2
Venn diagram
Let ℩ denote the entire probability space.
A âˆȘ B represents union =⇒ “Take everything in A and B”.
A ∩ B represents intersection =⇒ “Take common parts in A and B”.
c⃝ 2015 Math Academy www.MathAcademy.sg 3
Venn diagram
Let ℩ denote the entire probability space.
A âˆȘ B represents union =⇒ “Take everything in A and B”.
A ∩ B represents intersection =⇒ “Take common parts in A and B”.
c⃝ 2015 Math Academy www.MathAcademy.sg 4
.
Useful Results
..
.
(i) P(A) + P(Aâ€Č
) = 1
P(A|B) + P(Aâ€Č
|B) = 1.
℩
A B
(ii) P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B)
℩
A B
c⃝ 2015 Math Academy www.MathAcademy.sg 5
.
Useful Results
..
.
(iii) P(A âˆȘ B) = P(A) + P(B ∩ Aâ€Č
)
℩
A B
(iv) P(A) = P(A ∩ B) + P(A ∩ Bâ€Č
)
℩
A B
Recall also the following formula which would be useful for venn diagram
questions.
P(A|B) =
P(A ∩ B)
P(B)
or P(A|B) · P(B) = P(A ∩ B)
c⃝ 2015 Math Academy www.MathAcademy.sg 6
.
Useful Results
..
.
(iii) P(A âˆȘ B) = P(A) + P(B ∩ Aâ€Č
)
℩
A B
(iv) P(A) = P(A ∩ B) + P(A ∩ Bâ€Č
)
℩
A B
Recall also the following formula which would be useful for venn diagram
questions.
P(A|B) =
P(A ∩ B)
P(B)
or P(A|B) · P(B) = P(A ∩ B)
c⃝ 2015 Math Academy www.MathAcademy.sg 7
Mutually Exclusive VS Independence
Two events, A and B, are said to be mutually exclusive if and only if the 2 sets
are disjoint (no intersection between the 2 sets). That is,
P(A ∩ B) = 0.
Mutually exclusive events cannot happen at the same time.
℩
A B
Mutually exclusive
Two events, A and B, are said to be independent, if the
occurence/non-occurence of A does not aïŹ€ect B. The concept of independence
cannot be ‘seen’ on a venn-diagram. If A and B are independent, then
P(A ∩ B) = P(A) · P(B).
c⃝ 2015 Math Academy www.MathAcademy.sg 8
Mutually Exclusive VS Independence
Two events, A and B, are said to be mutually exclusive if and only if the 2 sets
are disjoint (no intersection between the 2 sets). That is,
P(A ∩ B) = 0.
Mutually exclusive events cannot happen at the same time.
℩
A B
Mutually exclusive
Two events, A and B, are said to be independent, if the
occurence/non-occurence of A does not aïŹ€ect B. The concept of independence
cannot be ‘seen’ on a venn-diagram. If A and B are independent, then
P(A ∩ B) = P(A) · P(B).
c⃝ 2015 Math Academy www.MathAcademy.sg 9
Mutually Exclusive VS Independence
Two events, A and B, are said to be mutually exclusive if and only if the 2 sets
are disjoint (no intersection between the 2 sets). That is,
P(A ∩ B) = 0.
Mutually exclusive events cannot happen at the same time.
℩
A B
Mutually exclusive
Two events, A and B, are said to be independent, if the
occurence/non-occurence of A does not aïŹ€ect B. The concept of independence
cannot be ‘seen’ on a venn-diagram. If A and B are independent, then
P(A ∩ B) = P(A) · P(B).
c⃝ 2015 Math Academy www.MathAcademy.sg 10
Mutually Exclusive VS Independence
Two events, A and B, are said to be mutually exclusive if and only if the 2 sets
are disjoint (no intersection between the 2 sets). That is,
P(A ∩ B) = 0.
Mutually exclusive events cannot happen at the same time.
℩
A B
Mutually exclusive
Two events, A and B, are said to be independent, if the
occurence/non-occurence of A does not aïŹ€ect B. The concept of independence
cannot be ‘seen’ on a venn-diagram. If A and B are independent, then
P(A ∩ B) = P(A) · P(B).
c⃝ 2015 Math Academy www.MathAcademy.sg 11
Mutually Exclusive VS Independence
Two events, A and B, are said to be mutually exclusive if and only if the 2 sets
are disjoint (no intersection between the 2 sets). That is,
P(A ∩ B) = 0.
Mutually exclusive events cannot happen at the same time.
℩
A B
Mutually exclusive
Two events, A and B, are said to be independent, if the
occurence/non-occurence of A does not aïŹ€ect B. The concept of independence
cannot be ‘seen’ on a venn-diagram. If A and B are independent, then
P(A ∩ B) = P(A) · P(B).
c⃝ 2015 Math Academy www.MathAcademy.sg 12
.
What to test for?
..
.
Mutually exclusive:
P(A ∩ B) = 0
Independent:
(1) P(A ∩ B) = P(A) · P(B)
(2) P(A|B) = P(A)
We can use either of the 2 equations to prove independence of two events.
c⃝ 2015 Math Academy www.MathAcademy.sg 13
.
What to test for?
..
.
Mutually exclusive:
P(A ∩ B) = 0
Independent:
(1) P(A ∩ B) = P(A) · P(B)
(2) P(A|B) = P(A)
We can use either of the 2 equations to prove independence of two events.
c⃝ 2015 Math Academy www.MathAcademy.sg 14
.
What to test for?
..
.
Mutually exclusive:
P(A ∩ B) = 0
Independent:
(1) P(A ∩ B) = P(A) · P(B)
(2) P(A|B) = P(A)
We can use either of the 2 equations to prove independence of two events.
c⃝ 2015 Math Academy www.MathAcademy.sg 15
.
What to test for?
..
.
Mutually exclusive:
P(A ∩ B) = 0
Independent:
(1) P(A ∩ B) = P(A) · P(B)
(2) P(A|B) = P(A)
We can use either of the 2 equations to prove independence of two events.
c⃝ 2015 Math Academy www.MathAcademy.sg 16
.
What to test for?
..
.
Mutually exclusive:
P(A ∩ B) = 0
Independent:
(1) P(A ∩ B) = P(A) · P(B)
(2) P(A|B) = P(A)
We can use either of the 2 equations to prove independence of two events.
c⃝ 2015 Math Academy www.MathAcademy.sg 17
.
Example (6)
..
.
A fair 6-sided die is thrown twice. Let A, B and C denote the following events:
A - The ïŹrst throw is ‘1’.
B - The ïŹrst throw is ‘2’.
C - The second throw is ‘1’.
Q. Are A and B mutually exclusive?
A. Yes. The 2 events cannot happen at the same time (a throw cannot be both
1 and 2). Hence P(A ∩ B) = 0.
Q. Are A and C independent?
A. Yes. The outcome of the ïŹrst throw does not aïŹ€ect the second throw.
Q. Are A and C mutually exclusive?
A. No. A and C can both happen together. P(A ∩ C) = 1
6
× 1
6
= 1
36
Ìž= 0.
c⃝ 2015 Math Academy www.MathAcademy.sg 18
.
Example (6)
..
.
A fair 6-sided die is thrown twice. Let A, B and C denote the following events:
A - The ïŹrst throw is ‘1’.
B - The ïŹrst throw is ‘2’.
C - The second throw is ‘1’.
Q. Are A and B mutually exclusive?
A. Yes. The 2 events cannot happen at the same time (a throw cannot be both
1 and 2). Hence P(A ∩ B) = 0.
Q. Are A and C independent?
A. Yes. The outcome of the ïŹrst throw does not aïŹ€ect the second throw.
Q. Are A and C mutually exclusive?
A. No. A and C can both happen together. P(A ∩ C) = 1
6
× 1
6
= 1
36
Ìž= 0.
c⃝ 2015 Math Academy www.MathAcademy.sg 19
.
Example (6)
..
.
A fair 6-sided die is thrown twice. Let A, B and C denote the following events:
A - The ïŹrst throw is ‘1’.
B - The ïŹrst throw is ‘2’.
C - The second throw is ‘1’.
Q. Are A and B mutually exclusive?
A. Yes. The 2 events cannot happen at the same time (a throw cannot be both
1 and 2). Hence P(A ∩ B) = 0.
Q. Are A and C independent?
A. Yes. The outcome of the ïŹrst throw does not aïŹ€ect the second throw.
Q. Are A and C mutually exclusive?
A. No. A and C can both happen together. P(A ∩ C) = 1
6
× 1
6
= 1
36
Ìž= 0.
c⃝ 2015 Math Academy www.MathAcademy.sg 20
.
Example (6)
..
.
A fair 6-sided die is thrown twice. Let A, B and C denote the following events:
A - The ïŹrst throw is ‘1’.
B - The ïŹrst throw is ‘2’.
C - The second throw is ‘1’.
Q. Are A and B mutually exclusive?
A. Yes. The 2 events cannot happen at the same time (a throw cannot be both
1 and 2). Hence P(A ∩ B) = 0.
Q. Are A and C independent?
A. Yes. The outcome of the ïŹrst throw does not aïŹ€ect the second throw.
Q. Are A and C mutually exclusive?
A. No. A and C can both happen together. P(A ∩ C) = 1
6
× 1
6
= 1
36
Ìž= 0.
c⃝ 2015 Math Academy www.MathAcademy.sg 21
.
Example (7)
..
.
For events A and B, it is given that P(A|Bâ€Č
) = 2
3
, P(A ∩ B) = 1
10
and
P(B) = 2
5
. Find,
(a) P(A ∩ Bâ€Č
)
(b) P(A)
(c) State whether A and B are independent, giving a reason for your answer.
(a)
P(A|Bâ€Č
) =
P(A ∩ Bâ€Č
)
P(Bâ€Č)
2
3
=
P(A ∩ Bâ€Č
)
1 − P(B)
2
3
=
P(A ∩ Bâ€Č
)
1 − 2
5
P(A ∩ Bâ€Č
) =
2
5
c⃝ 2015 Math Academy www.MathAcademy.sg 22
.
Example (7)
..
.
For events A and B, it is given that P(A|Bâ€Č
) = 2
3
, P(A ∩ B) = 1
10
and
P(B) = 2
5
. Find,
(a) P(A ∩ Bâ€Č
)
(b) P(A)
(c) State whether A and B are independent, giving a reason for your answer.
(a)
P(A|Bâ€Č
) =
P(A ∩ Bâ€Č
)
P(Bâ€Č)
2
3
=
P(A ∩ Bâ€Č
)
1 − P(B)
2
3
=
P(A ∩ Bâ€Č
)
1 − 2
5
P(A ∩ Bâ€Č
) =
2
5
c⃝ 2015 Math Academy www.MathAcademy.sg 23
.
Example (7)
..
.
For events A and B, it is given that P(A|Bâ€Č
) = 2
3
, P(A ∩ B) = 1
10
and
P(B) = 2
5
. Find,
(a) P(A ∩ Bâ€Č
)
(b) P(A)
(c) State whether A and B are independent, giving a reason for your answer.
(b)
P(A) = P(A ∩ B) + P(A ∩ Bâ€Č
)
=
1
10
+
2
5
=
1
2
(c) P(A ∩ B) = 1
10
and P(A)·P(B) = 1
2
· 2
5
= 1
5
.
Since P(A ∩ B) Ìž= P(A)·P(B), A and B are not independent.
c⃝ 2015 Math Academy www.MathAcademy.sg 24
.
Example (7)
..
.
For events A and B, it is given that P(A|Bâ€Č
) = 2
3
, P(A ∩ B) = 1
10
and
P(B) = 2
5
. Find,
(a) P(A ∩ Bâ€Č
)
(b) P(A)
(c) State whether A and B are independent, giving a reason for your answer.
(b)
P(A) = P(A ∩ B) + P(A ∩ Bâ€Č
)
=
1
10
+
2
5
=
1
2
(c) P(A ∩ B) = 1
10
and P(A)·P(B) = 1
2
· 2
5
= 1
5
.
Since P(A ∩ B) Ìž= P(A)·P(B), A and B are not independent.
c⃝ 2015 Math Academy www.MathAcademy.sg 25
.
Example (7)
..
.
For events A and B, it is given that P(A|Bâ€Č
) = 2
3
, P(A ∩ B) = 1
10
and
P(B) = 2
5
. Find,
(a) P(A ∩ Bâ€Č
)
(b) P(A)
(c) State whether A and B are independent, giving a reason for your answer.
(b)
P(A) = P(A ∩ B) + P(A ∩ Bâ€Č
)
=
1
10
+
2
5
=
1
2
(c) P(A ∩ B) = 1
10
and P(A)·P(B) = 1
2
· 2
5
= 1
5
.
Since P(A ∩ B) Ìž= P(A)·P(B), A and B are not independent.
c⃝ 2015 Math Academy www.MathAcademy.sg 26
.
Example (8)
..
.
Events A and B are such that P(A) = 1
7
, P(B|A) = 1
5
and P(A âˆȘ B) = 5
7
. Find
P(Aâ€Č
∩ Bâ€Č
) and P(B). State whether A and B are mutually exclusive, giving a
reason for your answer.
℩
A B
P(Aâ€Č
)
℩
A B
P(Bâ€Č
)
℩
A B
P(Aâ€Č
∩ Bâ€Č
)
P(Aâ€Č
∩ Bâ€Č
) = 1 − P(A âˆȘ B)
= 1 −
5
7
=
2
7
P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B)
5
7
=
1
7
+ P(B) − P(B|A) · P(A)
5
7
=
1
7
+ P(B) −
1
5
·
1
7
P(B) =
3
5
Since P(A ∩ B) = P(B|A) · P(A) = 1
5
· 1
7
Ìž= 0, A and B are not mutually
exclusive.
c⃝ 2015 Math Academy www.MathAcademy.sg 27
.
Example (8)
..
.
Events A and B are such that P(A) = 1
7
, P(B|A) = 1
5
and P(A âˆȘ B) = 5
7
. Find
P(Aâ€Č
∩ Bâ€Č
) and P(B). State whether A and B are mutually exclusive, giving a
reason for your answer.
℩
A B
P(Aâ€Č
)
℩
A B
P(Bâ€Č
)
℩
A B
P(Aâ€Č
∩ Bâ€Č
)
P(Aâ€Č
∩ Bâ€Č
) = 1 − P(A âˆȘ B)
= 1 −
5
7
=
2
7
P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B)
5
7
=
1
7
+ P(B) − P(B|A) · P(A)
5
7
=
1
7
+ P(B) −
1
5
·
1
7
P(B) =
3
5
Since P(A ∩ B) = P(B|A) · P(A) = 1
5
· 1
7
Ìž= 0, A and B are not mutually
exclusive.
c⃝ 2015 Math Academy www.MathAcademy.sg 28
.
Example (8)
..
.
Events A and B are such that P(A) = 1
7
, P(B|A) = 1
5
and P(A âˆȘ B) = 5
7
. Find
P(Aâ€Č
∩ Bâ€Č
) and P(B). State whether A and B are mutually exclusive, giving a
reason for your answer.
℩
A B
P(Aâ€Č
)
℩
A B
P(Bâ€Č
)
℩
A B
P(Aâ€Č
∩ Bâ€Č
)
P(Aâ€Č
∩ Bâ€Č
) = 1 − P(A âˆȘ B)
= 1 −
5
7
=
2
7
P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B)
5
7
=
1
7
+ P(B) − P(B|A) · P(A)
5
7
=
1
7
+ P(B) −
1
5
·
1
7
P(B) =
3
5
Since P(A ∩ B) = P(B|A) · P(A) = 1
5
· 1
7
Ìž= 0, A and B are not mutually
exclusive.
c⃝ 2015 Math Academy www.MathAcademy.sg 29
.
Example (8)
..
.
Events A and B are such that P(A) = 1
7
, P(B|A) = 1
5
and P(A âˆȘ B) = 5
7
. Find
P(Aâ€Č
∩ Bâ€Č
) and P(B). State whether A and B are mutually exclusive, giving a
reason for your answer.
℩
A B
P(Aâ€Č
)
℩
A B
P(Bâ€Č
)
℩
A B
P(Aâ€Č
∩ Bâ€Č
)
P(Aâ€Č
∩ Bâ€Č
) = 1 − P(A âˆȘ B)
= 1 −
5
7
=
2
7
P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B)
5
7
=
1
7
+ P(B) − P(B|A) · P(A)
5
7
=
1
7
+ P(B) −
1
5
·
1
7
P(B) =
3
5
Since P(A ∩ B) = P(B|A) · P(A) = 1
5
· 1
7
Ìž= 0, A and B are not mutually
exclusive.
c⃝ 2015 Math Academy www.MathAcademy.sg 30
.
Example (9)
..
.
Given that the two events A and B are such that P(A|B) = 2
3
, P(A ∩ Bâ€Č
) = 1
4
and P(A ∩ B) = 5
12
,
(a) determine if A and B are independent,
(b) ïŹnd P(A âˆȘ B).
[a) Yes b) 7
8
]
(a)
P(A) = P(A ∩ Bâ€Č
) + P(A ∩ B)
=
1
4
+
5
12
=
2
3
Since P(A|B) = P(A) = 2
3
, the two
events are independent.
(b)
We ïŹrst ïŹnd P(B).
P(A ∩ B) = P(A) · P(B)
5
12
=
2
3
· P(B)
P(B) =
5
8
P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B)
=
2
3
+
5
8
−
5
12
=
7
8
c⃝ 2015 Math Academy www.MathAcademy.sg 31
.
Example (9)
..
.
Given that the two events A and B are such that P(A|B) = 2
3
, P(A ∩ Bâ€Č
) = 1
4
and P(A ∩ B) = 5
12
,
(a) determine if A and B are independent,
(b) ïŹnd P(A âˆȘ B).
[a) Yes b) 7
8
]
(a)
P(A) = P(A ∩ Bâ€Č
) + P(A ∩ B)
=
1
4
+
5
12
=
2
3
Since P(A|B) = P(A) = 2
3
, the two
events are independent.
(b)
We ïŹrst ïŹnd P(B).
P(A ∩ B) = P(A) · P(B)
5
12
=
2
3
· P(B)
P(B) =
5
8
P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B)
=
2
3
+
5
8
−
5
12
=
7
8
c⃝ 2015 Math Academy www.MathAcademy.sg 32
.
Example (10)
..
.
There are 20 doctors and 15 engineers attending a conference. The number of
women doctors and that of women engineers are 12 and 5 respectively. Four
participants from this group are selected randomly to chair some sessions of
panel discussions.
(a) i) Find the probability that three doctors are selected.
ii) Suppose there are 4 positions in the group to be ïŹlled, namely, chair person,
vice-chair, secretary and executive. Find the probability that the chair,
vice-chair and secretary are doctors.
(a) i)
P(3 doctors are selected) =
20C3 ×15 C1
35C4
=
855
2618
ii)
P( chair, vice-chair and secretary are doctors) =
(20C3 × 3!) ×15 C1
35C4 × 4!
=
855
10472
c⃝ 2015 Math Academy www.MathAcademy.sg 33
.
Example (10)
..
.
There are 20 doctors and 15 engineers attending a conference. The number of
women doctors and that of women engineers are 12 and 5 respectively. Four
participants from this group are selected randomly to chair some sessions of
panel discussions.
(a) i) Find the probability that three doctors are selected.
ii) Suppose there are 4 positions in the group to be ïŹlled, namely, chair person,
vice-chair, secretary and executive. Find the probability that the chair,
vice-chair and secretary are doctors.
(a) i)
P(3 doctors are selected) =
20C3 ×15 C1
35C4
=
855
2618
ii)
P( chair, vice-chair and secretary are doctors) =
(20C3 × 3!) ×15 C1
35C4 × 4!
=
855
10472
c⃝ 2015 Math Academy www.MathAcademy.sg 34
.
Example (10)
..
.
There are 20 doctors and 15 engineers attending a conference. The number of
women doctors and that of women engineers are 12 and 5 respectively. Four
participants from this group are selected randomly to chair some sessions of
panel discussions.
(a) i) Find the probability that three doctors are selected.
ii) Suppose there are 4 positions in the group to be ïŹlled, namely, chair person,
vice-chair, secretary and executive. Find the probability that the chair,
vice-chair and secretary are doctors.
(a) i)
P(3 doctors are selected) =
20C3 ×15 C1
35C4
=
855
2618
ii)
P( chair, vice-chair and secretary are doctors) =
(20C3 × 3!) ×15 C1
35C4 × 4!
=
855
10472
c⃝ 2015 Math Academy www.MathAcademy.sg 35
.
Example (10)
..
.
There are 20 doctors and 15 engineers attending a conference. The number of
women doctors and that of women engineers are 12 and 5 respectively. Four
participants from this group are selected randomly to chair some sessions of
panel discussions.
(a) i) Find the probability that three doctors are selected.
ii) Suppose there are 4 positions in the group to be ïŹlled, namely, chair person,
vice-chair, secretary and executive. Find the probability that the chair,
vice-chair and secretary are doctors.
(a) i)
P(3 doctors are selected) =
20C3 ×15 C1
35C4
=
855
2618
ii)
P( chair, vice-chair and secretary are doctors) =
(20C3 × 3!) ×15 C1
35C4 × 4!
=
855
10472
c⃝ 2015 Math Academy www.MathAcademy.sg 36
.
Example (10)
..
.
There are 20 doctors and 15 engineers attending a conference. The number of
women doctors and that of women engineers are 12 and 5 respectively. Four
participants from this group are selected randomly to chair some sessions of
panel discussions.
(b) Given that two women are selected, ïŹnd the probability that both of them
are doctors.
(b)
P(Both are doctors | 2 women selected)
=
P(Both women selected are doctors)
P(2 women selected)
=
12
C2 ×18
C2
35C4
Ă·
17
C2 ×18
C2
35C4
=
33
68
c⃝ 2015 Math Academy www.MathAcademy.sg 37
.
Example (10)
..
.
There are 20 doctors and 15 engineers attending a conference. The number of
women doctors and that of women engineers are 12 and 5 respectively. Four
participants from this group are selected randomly to chair some sessions of
panel discussions.
(b) Given that two women are selected, ïŹnd the probability that both of them
are doctors.
(b)
P(Both are doctors | 2 women selected)
=
P(Both women selected are doctors)
P(2 women selected)
=
12
C2 ×18
C2
35C4
Ă·
17
C2 ×18
C2
35C4
=
33
68
c⃝ 2015 Math Academy www.MathAcademy.sg 38
.
Example (10)
..
.
There are 20 doctors and 15 engineers attending a conference. The number of
women doctors and that of women engineers are 12 and 5 respectively. Four
participants from this group are selected randomly to chair some sessions of
panel discussions.
(b) Given that two women are selected, ïŹnd the probability that both of them
are doctors.
(b)
P(Both are doctors | 2 women selected)
=
P(Both women selected are doctors)
P(2 women selected)
=
12
C2 ×18
C2
35C4
Ă·
17
C2 ×18
C2
35C4
=
33
68
c⃝ 2015 Math Academy www.MathAcademy.sg 39
.
Example (10)
..
.
There are 20 doctors and 15 engineers attending a conference. The number of
women doctors and that of women engineers are 12 and 5 respectively. Four
participants from this group are selected randomly to chair some sessions of
panel discussions.
(b) Given that two women are selected, ïŹnd the probability that both of them
are doctors.
(b)
P(Both are doctors | 2 women selected)
=
P(Both women selected are doctors)
P(2 women selected)
=
12
C2 ×18
C2
35C4
Ă·
17
C2 ×18
C2
35C4
=
33
68
c⃝ 2015 Math Academy www.MathAcademy.sg 40
.
Example (10)
..
.
There are 20 doctors and 15 engineers attending a conference. The number of
women doctors and that of women engineers are 12 and 5 respectively. Four
participants from this group are selected randomly to chair some sessions of
panel discussions.
(b) Given that two women are selected, ïŹnd the probability that both of them
are doctors.
(b)
P(Both are doctors | 2 women selected)
=
P(Both women selected are doctors)
P(2 women selected)
=
12
C2 ×18
C2
35C4
Ă·
17
C2 ×18
C2
35C4
=
33
68
c⃝ 2015 Math Academy www.MathAcademy.sg 41

Weitere Àhnliche Inhalte

Andere mochten auch

Lesson 11 2 experimental probability
Lesson 11 2 experimental probabilityLesson 11 2 experimental probability
Lesson 11 2 experimental probabilitymlabuski
 
Higher Maths 1.2.2 - Graphs and Transformations
Higher Maths 1.2.2 - Graphs and TransformationsHigher Maths 1.2.2 - Graphs and Transformations
Higher Maths 1.2.2 - Graphs and Transformationstimschmitz
 
4.4 probability of compound events
4.4 probability of compound events4.4 probability of compound events
4.4 probability of compound eventshisema01
 
Probability; Compound Event, Permutations
Probability; Compound Event, PermutationsProbability; Compound Event, Permutations
Probability; Compound Event, PermutationsReversearp
 
Annual lesson plan 2012
Annual lesson plan 2012Annual lesson plan 2012
Annual lesson plan 2012faziatul
 
Mathlessonplan
MathlessonplanMathlessonplan
MathlessonplanLouis Carpio
 
Probability and Statistics Notes - Akshansh
Probability and Statistics Notes - AkshanshProbability and Statistics Notes - Akshansh
Probability and Statistics Notes - AkshanshAkshansh Chaudhary
 
Math functions, relations, domain & range
Math functions, relations, domain & rangeMath functions, relations, domain & range
Math functions, relations, domain & rangeRenee Scott
 
PROBABILITY AND IT'S TYPES WITH RULES
PROBABILITY AND IT'S TYPES WITH RULESPROBABILITY AND IT'S TYPES WITH RULES
PROBABILITY AND IT'S TYPES WITH RULESBhargavi Bhanu
 
Probability ppt by Shivansh J.
Probability ppt by Shivansh J.Probability ppt by Shivansh J.
Probability ppt by Shivansh J.shivujagga
 
Basic Probability
Basic Probability Basic Probability
Basic Probability kaurab
 
Probability Overview
Probability OverviewProbability Overview
Probability Overviewmmeddin
 
Basic Concept Of Probability
Basic Concept Of ProbabilityBasic Concept Of Probability
Basic Concept Of Probabilityguest45a926
 
Search Engine Powerpoint
Search Engine PowerpointSearch Engine Powerpoint
Search Engine Powerpoint201014161
 
Final Year Project Presentation
Final Year Project PresentationFinal Year Project Presentation
Final Year Project PresentationSyed Absar
 
Discrete Mathematics - All chapters
Discrete Mathematics - All chapters Discrete Mathematics - All chapters
Discrete Mathematics - All chapters Omnia A. Abdullah
 
PROBABILITY
PROBABILITYPROBABILITY
PROBABILITYVIV13
 
Presentation on project report
Presentation on project reportPresentation on project report
Presentation on project reportramesh_x
 

Andere mochten auch (18)

Lesson 11 2 experimental probability
Lesson 11 2 experimental probabilityLesson 11 2 experimental probability
Lesson 11 2 experimental probability
 
Higher Maths 1.2.2 - Graphs and Transformations
Higher Maths 1.2.2 - Graphs and TransformationsHigher Maths 1.2.2 - Graphs and Transformations
Higher Maths 1.2.2 - Graphs and Transformations
 
4.4 probability of compound events
4.4 probability of compound events4.4 probability of compound events
4.4 probability of compound events
 
Probability; Compound Event, Permutations
Probability; Compound Event, PermutationsProbability; Compound Event, Permutations
Probability; Compound Event, Permutations
 
Annual lesson plan 2012
Annual lesson plan 2012Annual lesson plan 2012
Annual lesson plan 2012
 
Mathlessonplan
MathlessonplanMathlessonplan
Mathlessonplan
 
Probability and Statistics Notes - Akshansh
Probability and Statistics Notes - AkshanshProbability and Statistics Notes - Akshansh
Probability and Statistics Notes - Akshansh
 
Math functions, relations, domain & range
Math functions, relations, domain & rangeMath functions, relations, domain & range
Math functions, relations, domain & range
 
PROBABILITY AND IT'S TYPES WITH RULES
PROBABILITY AND IT'S TYPES WITH RULESPROBABILITY AND IT'S TYPES WITH RULES
PROBABILITY AND IT'S TYPES WITH RULES
 
Probability ppt by Shivansh J.
Probability ppt by Shivansh J.Probability ppt by Shivansh J.
Probability ppt by Shivansh J.
 
Basic Probability
Basic Probability Basic Probability
Basic Probability
 
Probability Overview
Probability OverviewProbability Overview
Probability Overview
 
Basic Concept Of Probability
Basic Concept Of ProbabilityBasic Concept Of Probability
Basic Concept Of Probability
 
Search Engine Powerpoint
Search Engine PowerpointSearch Engine Powerpoint
Search Engine Powerpoint
 
Final Year Project Presentation
Final Year Project PresentationFinal Year Project Presentation
Final Year Project Presentation
 
Discrete Mathematics - All chapters
Discrete Mathematics - All chapters Discrete Mathematics - All chapters
Discrete Mathematics - All chapters
 
PROBABILITY
PROBABILITYPROBABILITY
PROBABILITY
 
Presentation on project report
Presentation on project reportPresentation on project report
Presentation on project report
 

Ähnlich wie Probability 2 - Math Academy - JC H2 maths A levels

BHARAT & KAJAL.pptx
BHARAT & KAJAL.pptxBHARAT & KAJAL.pptx
BHARAT & KAJAL.pptxKunal639873
 
[Junoon - E - Jee] - Probability - 13th Nov.pdf
[Junoon - E - Jee] - Probability - 13th Nov.pdf[Junoon - E - Jee] - Probability - 13th Nov.pdf
[Junoon - E - Jee] - Probability - 13th Nov.pdfPrakashPatra7
 
Probability-11.pdf
Probability-11.pdfProbability-11.pdf
Probability-11.pdfAtikaAbdulhayee
 
Venn conditional
Venn conditionalVenn conditional
Venn conditionalMario Caredo
 
Set-notation--venn-diagrams-and-probability [Read-Only].pptx
Set-notation--venn-diagrams-and-probability [Read-Only].pptxSet-notation--venn-diagrams-and-probability [Read-Only].pptx
Set-notation--venn-diagrams-and-probability [Read-Only].pptxChepkirui Sharon
 
Set-notation--venn-diagrams-and-probability [Read-Only].pptx
Set-notation--venn-diagrams-and-probability [Read-Only].pptxSet-notation--venn-diagrams-and-probability [Read-Only].pptx
Set-notation--venn-diagrams-and-probability [Read-Only].pptxChepkirui Sharon
 
1 Probability Please read sections 3.1 – 3.3 in your .docx
 1 Probability   Please read sections 3.1 – 3.3 in your .docx 1 Probability   Please read sections 3.1 – 3.3 in your .docx
1 Probability Please read sections 3.1 – 3.3 in your .docxaryan532920
 
11.5 Independent and Dependent Events
11.5 Independent and Dependent Events11.5 Independent and Dependent Events
11.5 Independent and Dependent Eventssmiller5
 
Probability-06.pdf
Probability-06.pdfProbability-06.pdf
Probability-06.pdfAtikaAbdulhayee
 
Probability (gr.11)
Probability (gr.11)Probability (gr.11)
Probability (gr.11)Vukile Xhego
 
Basic concept of probability
Basic concept of probabilityBasic concept of probability
Basic concept of probabilityIkhlas Rahman
 
2.1PROBABILITY OF UNION OF TWO EVENTS.pptx
2.1PROBABILITY OF UNION OF TWO EVENTS.pptx2.1PROBABILITY OF UNION OF TWO EVENTS.pptx
2.1PROBABILITY OF UNION OF TWO EVENTS.pptxAILEENBAUTISTA23
 
[L3] - (JEE 2.0) - Probability - 22th Dec.pdf
[L3] - (JEE 2.0) - Probability - 22th Dec.pdf[L3] - (JEE 2.0) - Probability - 22th Dec.pdf
[L3] - (JEE 2.0) - Probability - 22th Dec.pdfArchitSingh90
 
Reliability-Engineering.pdf
Reliability-Engineering.pdfReliability-Engineering.pdf
Reliability-Engineering.pdfBakiyalakshmiR1
 
G10 Math Q4-Week 1- Mutually Exclusive.ppt
G10 Math Q4-Week 1- Mutually Exclusive.pptG10 Math Q4-Week 1- Mutually Exclusive.ppt
G10 Math Q4-Week 1- Mutually Exclusive.pptArnoldMillones4
 
probability-180324013552.pptx
probability-180324013552.pptxprobability-180324013552.pptx
probability-180324013552.pptxVukile Xhego
 
Chapter 3 SE 2015.pptx
Chapter 3 SE 2015.pptxChapter 3 SE 2015.pptx
Chapter 3 SE 2015.pptxmiki304759
 
independentevents
independenteventsindependentevents
independenteventsDinesh Kumar
 

Ähnlich wie Probability 2 - Math Academy - JC H2 maths A levels (20)

BHARAT & KAJAL.pptx
BHARAT & KAJAL.pptxBHARAT & KAJAL.pptx
BHARAT & KAJAL.pptx
 
[Junoon - E - Jee] - Probability - 13th Nov.pdf
[Junoon - E - Jee] - Probability - 13th Nov.pdf[Junoon - E - Jee] - Probability - 13th Nov.pdf
[Junoon - E - Jee] - Probability - 13th Nov.pdf
 
Probability-11.pdf
Probability-11.pdfProbability-11.pdf
Probability-11.pdf
 
Venn conditional
Venn conditionalVenn conditional
Venn conditional
 
Set-notation--venn-diagrams-and-probability [Read-Only].pptx
Set-notation--venn-diagrams-and-probability [Read-Only].pptxSet-notation--venn-diagrams-and-probability [Read-Only].pptx
Set-notation--venn-diagrams-and-probability [Read-Only].pptx
 
Set-notation--venn-diagrams-and-probability [Read-Only].pptx
Set-notation--venn-diagrams-and-probability [Read-Only].pptxSet-notation--venn-diagrams-and-probability [Read-Only].pptx
Set-notation--venn-diagrams-and-probability [Read-Only].pptx
 
1 Probability Please read sections 3.1 – 3.3 in your .docx
 1 Probability   Please read sections 3.1 – 3.3 in your .docx 1 Probability   Please read sections 3.1 – 3.3 in your .docx
1 Probability Please read sections 3.1 – 3.3 in your .docx
 
11.5 Independent and Dependent Events
11.5 Independent and Dependent Events11.5 Independent and Dependent Events
11.5 Independent and Dependent Events
 
Probability-06.pdf
Probability-06.pdfProbability-06.pdf
Probability-06.pdf
 
Probability (gr.11)
Probability (gr.11)Probability (gr.11)
Probability (gr.11)
 
Basic concept of probability
Basic concept of probabilityBasic concept of probability
Basic concept of probability
 
2.1PROBABILITY OF UNION OF TWO EVENTS.pptx
2.1PROBABILITY OF UNION OF TWO EVENTS.pptx2.1PROBABILITY OF UNION OF TWO EVENTS.pptx
2.1PROBABILITY OF UNION OF TWO EVENTS.pptx
 
[L3] - (JEE 2.0) - Probability - 22th Dec.pdf
[L3] - (JEE 2.0) - Probability - 22th Dec.pdf[L3] - (JEE 2.0) - Probability - 22th Dec.pdf
[L3] - (JEE 2.0) - Probability - 22th Dec.pdf
 
Reliability-Engineering.pdf
Reliability-Engineering.pdfReliability-Engineering.pdf
Reliability-Engineering.pdf
 
S244 10 Probability.ppt
S244 10 Probability.pptS244 10 Probability.ppt
S244 10 Probability.ppt
 
G10 Math Q4-Week 1- Mutually Exclusive.ppt
G10 Math Q4-Week 1- Mutually Exclusive.pptG10 Math Q4-Week 1- Mutually Exclusive.ppt
G10 Math Q4-Week 1- Mutually Exclusive.ppt
 
probability-180324013552.pptx
probability-180324013552.pptxprobability-180324013552.pptx
probability-180324013552.pptx
 
Chapter 3 SE 2015.pptx
Chapter 3 SE 2015.pptxChapter 3 SE 2015.pptx
Chapter 3 SE 2015.pptx
 
Probability Theory 7
Probability Theory 7Probability Theory 7
Probability Theory 7
 
independentevents
independenteventsindependentevents
independentevents
 

Mehr von Math Academy Singapore

Mehr von Math Academy Singapore (11)

Sec 3 A Maths Notes Indices
Sec 3 A Maths Notes IndicesSec 3 A Maths Notes Indices
Sec 3 A Maths Notes Indices
 
Sec 4 A Maths Notes Maxima Minima
Sec 4 A Maths Notes Maxima MinimaSec 4 A Maths Notes Maxima Minima
Sec 4 A Maths Notes Maxima Minima
 
Sec 3 E Maths Notes Coordinate Geometry
Sec 3 E Maths Notes Coordinate GeometrySec 3 E Maths Notes Coordinate Geometry
Sec 3 E Maths Notes Coordinate Geometry
 
Sec 3 A Maths Notes Indices
Sec 3 A Maths Notes IndicesSec 3 A Maths Notes Indices
Sec 3 A Maths Notes Indices
 
Sec 2 Maths Notes Change Subject
Sec 2 Maths Notes Change SubjectSec 2 Maths Notes Change Subject
Sec 2 Maths Notes Change Subject
 
Sec 1 Maths Notes Equations
Sec 1 Maths Notes EquationsSec 1 Maths Notes Equations
Sec 1 Maths Notes Equations
 
JC Vectors summary
JC Vectors summaryJC Vectors summary
JC Vectors summary
 
Functions JC H2 Maths
Functions JC H2 MathsFunctions JC H2 Maths
Functions JC H2 Maths
 
Vectors2
Vectors2Vectors2
Vectors2
 
Math academy-partial-fractions-notes
Math academy-partial-fractions-notesMath academy-partial-fractions-notes
Math academy-partial-fractions-notes
 
Recurrence
RecurrenceRecurrence
Recurrence
 

KĂŒrzlich hochgeladen

Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning ExhibitSociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibitjbellavia9
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptxMaritesTamaniVerdade
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Jisc
 
Jamworks pilot and AI at Jisc (20/03/2024)
Jamworks pilot and AI at Jisc (20/03/2024)Jamworks pilot and AI at Jisc (20/03/2024)
Jamworks pilot and AI at Jisc (20/03/2024)Jisc
 
Salient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functionsSalient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functionsKarakKing
 
TỔNG ÔN TáșŹP THI VÀO LỚP 10 MÔN TIáșŸNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGở Â...
TỔNG ÔN TáșŹP THI VÀO LỚP 10 MÔN TIáșŸNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGở Â...TỔNG ÔN TáșŹP THI VÀO LỚP 10 MÔN TIáșŸNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGở Â...
TỔNG ÔN TáșŹP THI VÀO LỚP 10 MÔN TIáșŸNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGở Â...Nguyen Thanh Tu Collection
 
How to setup Pycharm environment for Odoo 17.pptx
How to setup Pycharm environment for Odoo 17.pptxHow to setup Pycharm environment for Odoo 17.pptx
How to setup Pycharm environment for Odoo 17.pptxCeline George
 
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...ZurliaSoop
 
80 ĐỀ THI THỏ TUYỂN SINH TIáșŸNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...
80 ĐỀ THI THỏ TUYỂN SINH TIáșŸNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...80 ĐỀ THI THỏ TUYỂN SINH TIáșŸNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...
80 ĐỀ THI THỏ TUYỂN SINH TIáșŸNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...Nguyen Thanh Tu Collection
 
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfagholdier
 
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptxCOMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptxannathomasp01
 
Plant propagation: Sexual and Asexual propapagation.pptx
Plant propagation: Sexual and Asexual propapagation.pptxPlant propagation: Sexual and Asexual propapagation.pptx
Plant propagation: Sexual and Asexual propapagation.pptxUmeshTimilsina1
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxheathfieldcps1
 
How to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POSHow to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POSCeline George
 
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptxInterdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptxPooja Bhuva
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxDenish Jangid
 
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet  PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...Kodo Millet  PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...pradhanghanshyam7136
 
NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...
NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...
NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...Amil baba
 
ICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxAreebaZafar22
 
FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024Elizabeth Walsh
 

KĂŒrzlich hochgeladen (20)

Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning ExhibitSociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibit
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
 
Jamworks pilot and AI at Jisc (20/03/2024)
Jamworks pilot and AI at Jisc (20/03/2024)Jamworks pilot and AI at Jisc (20/03/2024)
Jamworks pilot and AI at Jisc (20/03/2024)
 
Salient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functionsSalient Features of India constitution especially power and functions
Salient Features of India constitution especially power and functions
 
TỔNG ÔN TáșŹP THI VÀO LỚP 10 MÔN TIáșŸNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGở Â...
TỔNG ÔN TáșŹP THI VÀO LỚP 10 MÔN TIáșŸNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGở Â...TỔNG ÔN TáșŹP THI VÀO LỚP 10 MÔN TIáșŸNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGở Â...
TỔNG ÔN TáșŹP THI VÀO LỚP 10 MÔN TIáșŸNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGở Â...
 
How to setup Pycharm environment for Odoo 17.pptx
How to setup Pycharm environment for Odoo 17.pptxHow to setup Pycharm environment for Odoo 17.pptx
How to setup Pycharm environment for Odoo 17.pptx
 
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...
 
80 ĐỀ THI THỏ TUYỂN SINH TIáșŸNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...
80 ĐỀ THI THỏ TUYỂN SINH TIáșŸNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...80 ĐỀ THI THỏ TUYỂN SINH TIáșŸNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...
80 ĐỀ THI THỏ TUYỂN SINH TIáșŸNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...
 
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
 
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptxCOMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
 
Plant propagation: Sexual and Asexual propapagation.pptx
Plant propagation: Sexual and Asexual propapagation.pptxPlant propagation: Sexual and Asexual propapagation.pptx
Plant propagation: Sexual and Asexual propapagation.pptx
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
 
How to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POSHow to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POS
 
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptxInterdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptx
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
 
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet  PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...Kodo Millet  PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
 
NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...
NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...
NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...
 
ICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptx
 
FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024
 

Probability 2 - Math Academy - JC H2 maths A levels

  • 1. Probability Lesson 2 www.MathAcademy.sg Mr Ian Ang c⃝ 2015 Math Academy www.MathAcademy.sg 1
  • 2. Venn diagram Let ℩ denote the entire probability space. A âˆȘ B represents union =⇒ “Take everything in A and B”. A ∩ B represents intersection =⇒ “Take common parts in A and B”. c⃝ 2015 Math Academy www.MathAcademy.sg 2
  • 3. Venn diagram Let ℩ denote the entire probability space. A âˆȘ B represents union =⇒ “Take everything in A and B”. A ∩ B represents intersection =⇒ “Take common parts in A and B”. c⃝ 2015 Math Academy www.MathAcademy.sg 3
  • 4. Venn diagram Let ℩ denote the entire probability space. A âˆȘ B represents union =⇒ “Take everything in A and B”. A ∩ B represents intersection =⇒ “Take common parts in A and B”. c⃝ 2015 Math Academy www.MathAcademy.sg 4
  • 5. . Useful Results .. . (i) P(A) + P(Aâ€Č ) = 1 P(A|B) + P(Aâ€Č |B) = 1. ℩ A B (ii) P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B) ℩ A B c⃝ 2015 Math Academy www.MathAcademy.sg 5
  • 6. . Useful Results .. . (iii) P(A âˆȘ B) = P(A) + P(B ∩ Aâ€Č ) ℩ A B (iv) P(A) = P(A ∩ B) + P(A ∩ Bâ€Č ) ℩ A B Recall also the following formula which would be useful for venn diagram questions. P(A|B) = P(A ∩ B) P(B) or P(A|B) · P(B) = P(A ∩ B) c⃝ 2015 Math Academy www.MathAcademy.sg 6
  • 7. . Useful Results .. . (iii) P(A âˆȘ B) = P(A) + P(B ∩ Aâ€Č ) ℩ A B (iv) P(A) = P(A ∩ B) + P(A ∩ Bâ€Č ) ℩ A B Recall also the following formula which would be useful for venn diagram questions. P(A|B) = P(A ∩ B) P(B) or P(A|B) · P(B) = P(A ∩ B) c⃝ 2015 Math Academy www.MathAcademy.sg 7
  • 8. Mutually Exclusive VS Independence Two events, A and B, are said to be mutually exclusive if and only if the 2 sets are disjoint (no intersection between the 2 sets). That is, P(A ∩ B) = 0. Mutually exclusive events cannot happen at the same time. ℩ A B Mutually exclusive Two events, A and B, are said to be independent, if the occurence/non-occurence of A does not aïŹ€ect B. The concept of independence cannot be ‘seen’ on a venn-diagram. If A and B are independent, then P(A ∩ B) = P(A) · P(B). c⃝ 2015 Math Academy www.MathAcademy.sg 8
  • 9. Mutually Exclusive VS Independence Two events, A and B, are said to be mutually exclusive if and only if the 2 sets are disjoint (no intersection between the 2 sets). That is, P(A ∩ B) = 0. Mutually exclusive events cannot happen at the same time. ℩ A B Mutually exclusive Two events, A and B, are said to be independent, if the occurence/non-occurence of A does not aïŹ€ect B. The concept of independence cannot be ‘seen’ on a venn-diagram. If A and B are independent, then P(A ∩ B) = P(A) · P(B). c⃝ 2015 Math Academy www.MathAcademy.sg 9
  • 10. Mutually Exclusive VS Independence Two events, A and B, are said to be mutually exclusive if and only if the 2 sets are disjoint (no intersection between the 2 sets). That is, P(A ∩ B) = 0. Mutually exclusive events cannot happen at the same time. ℩ A B Mutually exclusive Two events, A and B, are said to be independent, if the occurence/non-occurence of A does not aïŹ€ect B. The concept of independence cannot be ‘seen’ on a venn-diagram. If A and B are independent, then P(A ∩ B) = P(A) · P(B). c⃝ 2015 Math Academy www.MathAcademy.sg 10
  • 11. Mutually Exclusive VS Independence Two events, A and B, are said to be mutually exclusive if and only if the 2 sets are disjoint (no intersection between the 2 sets). That is, P(A ∩ B) = 0. Mutually exclusive events cannot happen at the same time. ℩ A B Mutually exclusive Two events, A and B, are said to be independent, if the occurence/non-occurence of A does not aïŹ€ect B. The concept of independence cannot be ‘seen’ on a venn-diagram. If A and B are independent, then P(A ∩ B) = P(A) · P(B). c⃝ 2015 Math Academy www.MathAcademy.sg 11
  • 12. Mutually Exclusive VS Independence Two events, A and B, are said to be mutually exclusive if and only if the 2 sets are disjoint (no intersection between the 2 sets). That is, P(A ∩ B) = 0. Mutually exclusive events cannot happen at the same time. ℩ A B Mutually exclusive Two events, A and B, are said to be independent, if the occurence/non-occurence of A does not aïŹ€ect B. The concept of independence cannot be ‘seen’ on a venn-diagram. If A and B are independent, then P(A ∩ B) = P(A) · P(B). c⃝ 2015 Math Academy www.MathAcademy.sg 12
  • 13. . What to test for? .. . Mutually exclusive: P(A ∩ B) = 0 Independent: (1) P(A ∩ B) = P(A) · P(B) (2) P(A|B) = P(A) We can use either of the 2 equations to prove independence of two events. c⃝ 2015 Math Academy www.MathAcademy.sg 13
  • 14. . What to test for? .. . Mutually exclusive: P(A ∩ B) = 0 Independent: (1) P(A ∩ B) = P(A) · P(B) (2) P(A|B) = P(A) We can use either of the 2 equations to prove independence of two events. c⃝ 2015 Math Academy www.MathAcademy.sg 14
  • 15. . What to test for? .. . Mutually exclusive: P(A ∩ B) = 0 Independent: (1) P(A ∩ B) = P(A) · P(B) (2) P(A|B) = P(A) We can use either of the 2 equations to prove independence of two events. c⃝ 2015 Math Academy www.MathAcademy.sg 15
  • 16. . What to test for? .. . Mutually exclusive: P(A ∩ B) = 0 Independent: (1) P(A ∩ B) = P(A) · P(B) (2) P(A|B) = P(A) We can use either of the 2 equations to prove independence of two events. c⃝ 2015 Math Academy www.MathAcademy.sg 16
  • 17. . What to test for? .. . Mutually exclusive: P(A ∩ B) = 0 Independent: (1) P(A ∩ B) = P(A) · P(B) (2) P(A|B) = P(A) We can use either of the 2 equations to prove independence of two events. c⃝ 2015 Math Academy www.MathAcademy.sg 17
  • 18. . Example (6) .. . A fair 6-sided die is thrown twice. Let A, B and C denote the following events: A - The ïŹrst throw is ‘1’. B - The ïŹrst throw is ‘2’. C - The second throw is ‘1’. Q. Are A and B mutually exclusive? A. Yes. The 2 events cannot happen at the same time (a throw cannot be both 1 and 2). Hence P(A ∩ B) = 0. Q. Are A and C independent? A. Yes. The outcome of the ïŹrst throw does not aïŹ€ect the second throw. Q. Are A and C mutually exclusive? A. No. A and C can both happen together. P(A ∩ C) = 1 6 × 1 6 = 1 36 Ìž= 0. c⃝ 2015 Math Academy www.MathAcademy.sg 18
  • 19. . Example (6) .. . A fair 6-sided die is thrown twice. Let A, B and C denote the following events: A - The ïŹrst throw is ‘1’. B - The ïŹrst throw is ‘2’. C - The second throw is ‘1’. Q. Are A and B mutually exclusive? A. Yes. The 2 events cannot happen at the same time (a throw cannot be both 1 and 2). Hence P(A ∩ B) = 0. Q. Are A and C independent? A. Yes. The outcome of the ïŹrst throw does not aïŹ€ect the second throw. Q. Are A and C mutually exclusive? A. No. A and C can both happen together. P(A ∩ C) = 1 6 × 1 6 = 1 36 Ìž= 0. c⃝ 2015 Math Academy www.MathAcademy.sg 19
  • 20. . Example (6) .. . A fair 6-sided die is thrown twice. Let A, B and C denote the following events: A - The ïŹrst throw is ‘1’. B - The ïŹrst throw is ‘2’. C - The second throw is ‘1’. Q. Are A and B mutually exclusive? A. Yes. The 2 events cannot happen at the same time (a throw cannot be both 1 and 2). Hence P(A ∩ B) = 0. Q. Are A and C independent? A. Yes. The outcome of the ïŹrst throw does not aïŹ€ect the second throw. Q. Are A and C mutually exclusive? A. No. A and C can both happen together. P(A ∩ C) = 1 6 × 1 6 = 1 36 Ìž= 0. c⃝ 2015 Math Academy www.MathAcademy.sg 20
  • 21. . Example (6) .. . A fair 6-sided die is thrown twice. Let A, B and C denote the following events: A - The ïŹrst throw is ‘1’. B - The ïŹrst throw is ‘2’. C - The second throw is ‘1’. Q. Are A and B mutually exclusive? A. Yes. The 2 events cannot happen at the same time (a throw cannot be both 1 and 2). Hence P(A ∩ B) = 0. Q. Are A and C independent? A. Yes. The outcome of the ïŹrst throw does not aïŹ€ect the second throw. Q. Are A and C mutually exclusive? A. No. A and C can both happen together. P(A ∩ C) = 1 6 × 1 6 = 1 36 Ìž= 0. c⃝ 2015 Math Academy www.MathAcademy.sg 21
  • 22. . Example (7) .. . For events A and B, it is given that P(A|Bâ€Č ) = 2 3 , P(A ∩ B) = 1 10 and P(B) = 2 5 . Find, (a) P(A ∩ Bâ€Č ) (b) P(A) (c) State whether A and B are independent, giving a reason for your answer. (a) P(A|Bâ€Č ) = P(A ∩ Bâ€Č ) P(Bâ€Č) 2 3 = P(A ∩ Bâ€Č ) 1 − P(B) 2 3 = P(A ∩ Bâ€Č ) 1 − 2 5 P(A ∩ Bâ€Č ) = 2 5 c⃝ 2015 Math Academy www.MathAcademy.sg 22
  • 23. . Example (7) .. . For events A and B, it is given that P(A|Bâ€Č ) = 2 3 , P(A ∩ B) = 1 10 and P(B) = 2 5 . Find, (a) P(A ∩ Bâ€Č ) (b) P(A) (c) State whether A and B are independent, giving a reason for your answer. (a) P(A|Bâ€Č ) = P(A ∩ Bâ€Č ) P(Bâ€Č) 2 3 = P(A ∩ Bâ€Č ) 1 − P(B) 2 3 = P(A ∩ Bâ€Č ) 1 − 2 5 P(A ∩ Bâ€Č ) = 2 5 c⃝ 2015 Math Academy www.MathAcademy.sg 23
  • 24. . Example (7) .. . For events A and B, it is given that P(A|Bâ€Č ) = 2 3 , P(A ∩ B) = 1 10 and P(B) = 2 5 . Find, (a) P(A ∩ Bâ€Č ) (b) P(A) (c) State whether A and B are independent, giving a reason for your answer. (b) P(A) = P(A ∩ B) + P(A ∩ Bâ€Č ) = 1 10 + 2 5 = 1 2 (c) P(A ∩ B) = 1 10 and P(A)·P(B) = 1 2 · 2 5 = 1 5 . Since P(A ∩ B) Ìž= P(A)·P(B), A and B are not independent. c⃝ 2015 Math Academy www.MathAcademy.sg 24
  • 25. . Example (7) .. . For events A and B, it is given that P(A|Bâ€Č ) = 2 3 , P(A ∩ B) = 1 10 and P(B) = 2 5 . Find, (a) P(A ∩ Bâ€Č ) (b) P(A) (c) State whether A and B are independent, giving a reason for your answer. (b) P(A) = P(A ∩ B) + P(A ∩ Bâ€Č ) = 1 10 + 2 5 = 1 2 (c) P(A ∩ B) = 1 10 and P(A)·P(B) = 1 2 · 2 5 = 1 5 . Since P(A ∩ B) Ìž= P(A)·P(B), A and B are not independent. c⃝ 2015 Math Academy www.MathAcademy.sg 25
  • 26. . Example (7) .. . For events A and B, it is given that P(A|Bâ€Č ) = 2 3 , P(A ∩ B) = 1 10 and P(B) = 2 5 . Find, (a) P(A ∩ Bâ€Č ) (b) P(A) (c) State whether A and B are independent, giving a reason for your answer. (b) P(A) = P(A ∩ B) + P(A ∩ Bâ€Č ) = 1 10 + 2 5 = 1 2 (c) P(A ∩ B) = 1 10 and P(A)·P(B) = 1 2 · 2 5 = 1 5 . Since P(A ∩ B) Ìž= P(A)·P(B), A and B are not independent. c⃝ 2015 Math Academy www.MathAcademy.sg 26
  • 27. . Example (8) .. . Events A and B are such that P(A) = 1 7 , P(B|A) = 1 5 and P(A âˆȘ B) = 5 7 . Find P(Aâ€Č ∩ Bâ€Č ) and P(B). State whether A and B are mutually exclusive, giving a reason for your answer. ℩ A B P(Aâ€Č ) ℩ A B P(Bâ€Č ) ℩ A B P(Aâ€Č ∩ Bâ€Č ) P(Aâ€Č ∩ Bâ€Č ) = 1 − P(A âˆȘ B) = 1 − 5 7 = 2 7 P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B) 5 7 = 1 7 + P(B) − P(B|A) · P(A) 5 7 = 1 7 + P(B) − 1 5 · 1 7 P(B) = 3 5 Since P(A ∩ B) = P(B|A) · P(A) = 1 5 · 1 7 Ìž= 0, A and B are not mutually exclusive. c⃝ 2015 Math Academy www.MathAcademy.sg 27
  • 28. . Example (8) .. . Events A and B are such that P(A) = 1 7 , P(B|A) = 1 5 and P(A âˆȘ B) = 5 7 . Find P(Aâ€Č ∩ Bâ€Č ) and P(B). State whether A and B are mutually exclusive, giving a reason for your answer. ℩ A B P(Aâ€Č ) ℩ A B P(Bâ€Č ) ℩ A B P(Aâ€Č ∩ Bâ€Č ) P(Aâ€Č ∩ Bâ€Č ) = 1 − P(A âˆȘ B) = 1 − 5 7 = 2 7 P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B) 5 7 = 1 7 + P(B) − P(B|A) · P(A) 5 7 = 1 7 + P(B) − 1 5 · 1 7 P(B) = 3 5 Since P(A ∩ B) = P(B|A) · P(A) = 1 5 · 1 7 Ìž= 0, A and B are not mutually exclusive. c⃝ 2015 Math Academy www.MathAcademy.sg 28
  • 29. . Example (8) .. . Events A and B are such that P(A) = 1 7 , P(B|A) = 1 5 and P(A âˆȘ B) = 5 7 . Find P(Aâ€Č ∩ Bâ€Č ) and P(B). State whether A and B are mutually exclusive, giving a reason for your answer. ℩ A B P(Aâ€Č ) ℩ A B P(Bâ€Č ) ℩ A B P(Aâ€Č ∩ Bâ€Č ) P(Aâ€Č ∩ Bâ€Č ) = 1 − P(A âˆȘ B) = 1 − 5 7 = 2 7 P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B) 5 7 = 1 7 + P(B) − P(B|A) · P(A) 5 7 = 1 7 + P(B) − 1 5 · 1 7 P(B) = 3 5 Since P(A ∩ B) = P(B|A) · P(A) = 1 5 · 1 7 Ìž= 0, A and B are not mutually exclusive. c⃝ 2015 Math Academy www.MathAcademy.sg 29
  • 30. . Example (8) .. . Events A and B are such that P(A) = 1 7 , P(B|A) = 1 5 and P(A âˆȘ B) = 5 7 . Find P(Aâ€Č ∩ Bâ€Č ) and P(B). State whether A and B are mutually exclusive, giving a reason for your answer. ℩ A B P(Aâ€Č ) ℩ A B P(Bâ€Č ) ℩ A B P(Aâ€Č ∩ Bâ€Č ) P(Aâ€Č ∩ Bâ€Č ) = 1 − P(A âˆȘ B) = 1 − 5 7 = 2 7 P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B) 5 7 = 1 7 + P(B) − P(B|A) · P(A) 5 7 = 1 7 + P(B) − 1 5 · 1 7 P(B) = 3 5 Since P(A ∩ B) = P(B|A) · P(A) = 1 5 · 1 7 Ìž= 0, A and B are not mutually exclusive. c⃝ 2015 Math Academy www.MathAcademy.sg 30
  • 31. . Example (9) .. . Given that the two events A and B are such that P(A|B) = 2 3 , P(A ∩ Bâ€Č ) = 1 4 and P(A ∩ B) = 5 12 , (a) determine if A and B are independent, (b) ïŹnd P(A âˆȘ B). [a) Yes b) 7 8 ] (a) P(A) = P(A ∩ Bâ€Č ) + P(A ∩ B) = 1 4 + 5 12 = 2 3 Since P(A|B) = P(A) = 2 3 , the two events are independent. (b) We ïŹrst ïŹnd P(B). P(A ∩ B) = P(A) · P(B) 5 12 = 2 3 · P(B) P(B) = 5 8 P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B) = 2 3 + 5 8 − 5 12 = 7 8 c⃝ 2015 Math Academy www.MathAcademy.sg 31
  • 32. . Example (9) .. . Given that the two events A and B are such that P(A|B) = 2 3 , P(A ∩ Bâ€Č ) = 1 4 and P(A ∩ B) = 5 12 , (a) determine if A and B are independent, (b) ïŹnd P(A âˆȘ B). [a) Yes b) 7 8 ] (a) P(A) = P(A ∩ Bâ€Č ) + P(A ∩ B) = 1 4 + 5 12 = 2 3 Since P(A|B) = P(A) = 2 3 , the two events are independent. (b) We ïŹrst ïŹnd P(B). P(A ∩ B) = P(A) · P(B) 5 12 = 2 3 · P(B) P(B) = 5 8 P(A âˆȘ B) = P(A) + P(B) − P(A ∩ B) = 2 3 + 5 8 − 5 12 = 7 8 c⃝ 2015 Math Academy www.MathAcademy.sg 32
  • 33. . Example (10) .. . There are 20 doctors and 15 engineers attending a conference. The number of women doctors and that of women engineers are 12 and 5 respectively. Four participants from this group are selected randomly to chair some sessions of panel discussions. (a) i) Find the probability that three doctors are selected. ii) Suppose there are 4 positions in the group to be ïŹlled, namely, chair person, vice-chair, secretary and executive. Find the probability that the chair, vice-chair and secretary are doctors. (a) i) P(3 doctors are selected) = 20C3 ×15 C1 35C4 = 855 2618 ii) P( chair, vice-chair and secretary are doctors) = (20C3 × 3!) ×15 C1 35C4 × 4! = 855 10472 c⃝ 2015 Math Academy www.MathAcademy.sg 33
  • 34. . Example (10) .. . There are 20 doctors and 15 engineers attending a conference. The number of women doctors and that of women engineers are 12 and 5 respectively. Four participants from this group are selected randomly to chair some sessions of panel discussions. (a) i) Find the probability that three doctors are selected. ii) Suppose there are 4 positions in the group to be ïŹlled, namely, chair person, vice-chair, secretary and executive. Find the probability that the chair, vice-chair and secretary are doctors. (a) i) P(3 doctors are selected) = 20C3 ×15 C1 35C4 = 855 2618 ii) P( chair, vice-chair and secretary are doctors) = (20C3 × 3!) ×15 C1 35C4 × 4! = 855 10472 c⃝ 2015 Math Academy www.MathAcademy.sg 34
  • 35. . Example (10) .. . There are 20 doctors and 15 engineers attending a conference. The number of women doctors and that of women engineers are 12 and 5 respectively. Four participants from this group are selected randomly to chair some sessions of panel discussions. (a) i) Find the probability that three doctors are selected. ii) Suppose there are 4 positions in the group to be ïŹlled, namely, chair person, vice-chair, secretary and executive. Find the probability that the chair, vice-chair and secretary are doctors. (a) i) P(3 doctors are selected) = 20C3 ×15 C1 35C4 = 855 2618 ii) P( chair, vice-chair and secretary are doctors) = (20C3 × 3!) ×15 C1 35C4 × 4! = 855 10472 c⃝ 2015 Math Academy www.MathAcademy.sg 35
  • 36. . Example (10) .. . There are 20 doctors and 15 engineers attending a conference. The number of women doctors and that of women engineers are 12 and 5 respectively. Four participants from this group are selected randomly to chair some sessions of panel discussions. (a) i) Find the probability that three doctors are selected. ii) Suppose there are 4 positions in the group to be ïŹlled, namely, chair person, vice-chair, secretary and executive. Find the probability that the chair, vice-chair and secretary are doctors. (a) i) P(3 doctors are selected) = 20C3 ×15 C1 35C4 = 855 2618 ii) P( chair, vice-chair and secretary are doctors) = (20C3 × 3!) ×15 C1 35C4 × 4! = 855 10472 c⃝ 2015 Math Academy www.MathAcademy.sg 36
  • 37. . Example (10) .. . There are 20 doctors and 15 engineers attending a conference. The number of women doctors and that of women engineers are 12 and 5 respectively. Four participants from this group are selected randomly to chair some sessions of panel discussions. (b) Given that two women are selected, ïŹnd the probability that both of them are doctors. (b) P(Both are doctors | 2 women selected) = P(Both women selected are doctors) P(2 women selected) = 12 C2 ×18 C2 35C4 Ă· 17 C2 ×18 C2 35C4 = 33 68 c⃝ 2015 Math Academy www.MathAcademy.sg 37
  • 38. . Example (10) .. . There are 20 doctors and 15 engineers attending a conference. The number of women doctors and that of women engineers are 12 and 5 respectively. Four participants from this group are selected randomly to chair some sessions of panel discussions. (b) Given that two women are selected, ïŹnd the probability that both of them are doctors. (b) P(Both are doctors | 2 women selected) = P(Both women selected are doctors) P(2 women selected) = 12 C2 ×18 C2 35C4 Ă· 17 C2 ×18 C2 35C4 = 33 68 c⃝ 2015 Math Academy www.MathAcademy.sg 38
  • 39. . Example (10) .. . There are 20 doctors and 15 engineers attending a conference. The number of women doctors and that of women engineers are 12 and 5 respectively. Four participants from this group are selected randomly to chair some sessions of panel discussions. (b) Given that two women are selected, ïŹnd the probability that both of them are doctors. (b) P(Both are doctors | 2 women selected) = P(Both women selected are doctors) P(2 women selected) = 12 C2 ×18 C2 35C4 Ă· 17 C2 ×18 C2 35C4 = 33 68 c⃝ 2015 Math Academy www.MathAcademy.sg 39
  • 40. . Example (10) .. . There are 20 doctors and 15 engineers attending a conference. The number of women doctors and that of women engineers are 12 and 5 respectively. Four participants from this group are selected randomly to chair some sessions of panel discussions. (b) Given that two women are selected, ïŹnd the probability that both of them are doctors. (b) P(Both are doctors | 2 women selected) = P(Both women selected are doctors) P(2 women selected) = 12 C2 ×18 C2 35C4 Ă· 17 C2 ×18 C2 35C4 = 33 68 c⃝ 2015 Math Academy www.MathAcademy.sg 40
  • 41. . Example (10) .. . There are 20 doctors and 15 engineers attending a conference. The number of women doctors and that of women engineers are 12 and 5 respectively. Four participants from this group are selected randomly to chair some sessions of panel discussions. (b) Given that two women are selected, ïŹnd the probability that both of them are doctors. (b) P(Both are doctors | 2 women selected) = P(Both women selected are doctors) P(2 women selected) = 12 C2 ×18 C2 35C4 Ă· 17 C2 ×18 C2 35C4 = 33 68 c⃝ 2015 Math Academy www.MathAcademy.sg 41