SlideShare a Scribd company logo
1 of 11
Download to read offline
PROBABILITY
MULTIPLICATION LAW
DEPENDENT EVENTS
08
1
MULTIPLICATIVE LAW FOR DEPENDENT EVENTS
If A and B are two dependent events in a sample spaces S , then the probability that they will both
occur is equal to the probability that one of the events will occur multiplied by the conditional
probability that other event will occur given that one event has already occurred.
P(A  B) = P(A)P(B/A) Provided P(A) 0
P(B  A) = P(B)P(A/B) Provided P(B) 0
Number of outcomes in (A  B) = m3
Therefore, P(A) =
m1
n P(B) =
m2
n P(A  B) =
m3
n
Let there are n equally likely outcomes in a sample space S.
Number of outcomes in event A = m1
S
m1
A  B
A B
m2
m3
n
Number of outcome in event B = m2
Take P(A  B) =
m3
n
and multiply and divide on R.H.S by m1,
we have P(A  B) =
m3
n ×
m1
m1
=
m1
n ×
m3
m1
The ratio m3/m1 is the conditional probability of event B given A has occurred, that is, P(B/A).
Thus P(A  B) = P(A) P(B/A)
Take P(A  B) =
m3
n
and multiply and divide on R.H.S by m2, we have
P(A  B) =
m3
n
×
m2
m2
=
m2
n
×
m3
m2
The ratio m3/m2 is the conditional probability of event A given B has occurred, that is, P(A/B)
Thus P(A  B) = P(B) P(A/B) Amjad Ali
2
PROBLEM 48
A bag contains four red and three black balls. Find the probability that a red ball is drawn in
the first draw and a black ball in the second in two consecutive draws, without replacement
from the bag.
Red Black Total
4 3 7
A: Red ball on first draw
P(A) = 4/7
B: Black ball on second draw
P(B/A) = 3/6
P(A  B) = P(A)P(B/A)
P(A  B) = (4/7)(3/6)
P(A  B) = 2/7
Red Black Total
4 3 7
1st draw 1 0
3 3 6
Amjad Ali
3
PROBLEM 49
Find the probability of drawing a face (pictured) card on each of two consecutive draws from
a standard pack, without replacement of the first card.
Face cards Rest cards Total
12 40 52
A: Selecting a pictured card on first draw
P(A) = 12/52
B: Selecting a pictured card on second draw
P(B/A) = 11/51
P(A  B) = P(A)P(B/A)
P(A  B) = (12/52)(11/51)
P(A  B) = 11/221
Face Rest Total
12 40 52
1st draw 1 0
11 40 51
Amjad Ali
4
PROBLEM 50
A box contains 15 items, 4 of which are defective and 11 are good. Two items are selected.
What is the probability that the first is good and second is defective?
Defective Good Total
4 11 15
A: First is good
P(A) = 11/15
B: Second is defective
P(B/A) = 4/14
P(A  B) = P(A)P(B/A)
P(A  B) = (11/15)(4/14)
P(A  B) = 22/105
Defective Good Total
4 11 15
1st item 0 1
4 10 14
Amjad Ali
5
PROBLEM 51
Two balls are drawn in succession without replacement from a bag containing 5 white and 8
black balls. What is the probability that both balls are black?
White Black Total
5 8 13
A: Black ball on first draw
P(A) = 8/13
B: Black ball on second draw
P(B/A) = 7/12
P(A  B) = P(A)P(B/A)
P(A  B) = (8/13)(7/12)
P(A  B) = 14/39
White Black Total
5 8 13
1st draw 0 1
5 7 12
Amjad Ali
6
PROBLEM 52
Two drawing each of three balls is made from a bag containing 5 white and 8 black balls; the
balls are not being replaced before the next trial. What is the probability that the first
drawing will give 3 white balls and the second 3 black balls?
White Black Total
5 8 13
A: 3 white balls on first draw
143
5
C
C
.
C
P(A)
3
13
0
8
3
5


White Black Total
5 8 13
1st draw 3 0
2 8 10
2nd draw 0 3
B: 3 black balls on second draw
15
7
C
C
.
C
P(B/A)
3
10
3
8
0
2


P(B/A)
.
P(A)
B)
P(A 

429
7
15
7
.
143
5
B)
P(A 


Amjad Ali
7
PROBLEM 53
Show that the multiplication law P(AB) = P(A) P(B/ A), established for two events, may be
generalized to three events as follows;
P (A  B  C) = P (A) P (B/ A) P (C/ A  B)
Let A  B = K
Then A  B  C = K  C
P(A  B  C) = P(K  C)
= P(K ) P(C/ K)
= P(A  B ) P(C/ K)
= P(A ) P(B/A ) P(C/ A  B)
PROBLEM 54
Show that the multiplication law P(A  B) = P(B) P(A/ B), established for two events, may be
generalized to three events as follows;
P (A  B  C) = P (A/ B  C) P (B/ C) P ( C )
Let B  C = K
Then A  B  C = A  K
P(A  B  C) = P(A  K)
= P(K ) P(A/ K)
= P(B  C ) P(A/ K)
= P(C ) P(B/C ) P(A/ B  C)
= P(A/ B  C) P(B/C ) P(C ) Amjad Ali
8
PROBLEM 55
A farmer has a box containing 30 eggs, five of which have blood spots, he checks eggs by
taking them at random one after another from the box what is the probability that the first
two eggs have spots and third will be clear.
Spotted Clear Total
5 25 30
A: First is blood spotted
P(A) = 5/30
A: Second is blood spotted
P(B/A) = 4/29
C: Third is clear (good)
P(C/ A  B) = 25/28
P(A  B  C) = P(A) P(B/A) P(C/A  B)
P(A  B  C) = (5/30) (4/29) (25/28) = 25/1218
Spotted Clear Total
5 25 30
First 1 0
4 25 29
Second 1 0
3 25 28
Third 0 1
Amjad Ali
9
PROBLEM 56
Three cards are drawn in succession without replacement from an ordinary deck of playing
cards. Find the probability that the first card is a red ace, the second card is a ten or jack,
and the third card is greater than three but less than seven.
A: First is a red ace
P(A) = 2/52
B: Second is a ten or jack
P(B/A) = 8/51
C: Third is greater than 3 but less than 7
P(C/A  B) = 12/50
P(A  B  C) = P(A) P(B/A) P(C/A  B)
P(A  B  C) = (2/52) (8/51) (12/50) = 8/5525
Amjad Ali
10
THANK
YOU
O8
Amjad Ali
11

More Related Content

Similar to Probability-08.pdf

Conditional-Probability-Powerpoint.pptx
Conditional-Probability-Powerpoint.pptxConditional-Probability-Powerpoint.pptx
Conditional-Probability-Powerpoint.pptxVilDom
 
T3 probability
T3   probabilityT3   probability
T3 probabilitydeepushaw
 
5. probability qt 1st tri semester
5. probability qt 1st tri semester 5. probability qt 1st tri semester
5. probability qt 1st tri semester Karan Kukreja
 
MATHS_PROBALITY_CIA_SEM-2[1].pptx
MATHS_PROBALITY_CIA_SEM-2[1].pptxMATHS_PROBALITY_CIA_SEM-2[1].pptx
MATHS_PROBALITY_CIA_SEM-2[1].pptxSIDDHARTBHANSALI
 
Probability basics
Probability   basicsProbability   basics
Probability basicsSavi Arora
 
Chapter 3 SE 2015.pptx
Chapter 3 SE 2015.pptxChapter 3 SE 2015.pptx
Chapter 3 SE 2015.pptxmiki304759
 
Applied statistics and probability for engineers solution montgomery && runger
Applied statistics and probability for engineers solution   montgomery && rungerApplied statistics and probability for engineers solution   montgomery && runger
Applied statistics and probability for engineers solution montgomery && rungerAnkit Katiyar
 
R4 m.s. radhakrishnan, probability & statistics, dlpd notes.
R4 m.s. radhakrishnan, probability & statistics, dlpd notes.R4 m.s. radhakrishnan, probability & statistics, dlpd notes.
R4 m.s. radhakrishnan, probability & statistics, dlpd notes.Ramachandran Uthirapathi R
 
Introduction of Probability
Introduction of ProbabilityIntroduction of Probability
Introduction of Probabilityrey castro
 
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
 
Probability_Mutually exclusive event.ppt
Probability_Mutually exclusive event.pptProbability_Mutually exclusive event.ppt
Probability_Mutually exclusive event.pptWaiTengChoo
 
Probability(not mutually exclusive events)
Probability(not mutually exclusive events)Probability(not mutually exclusive events)
Probability(not mutually exclusive events)Nadeem Uddin
 
Information Theory and coding - Lecture 1
Information Theory and coding - Lecture 1Information Theory and coding - Lecture 1
Information Theory and coding - Lecture 1Aref35
 

Similar to Probability-08.pdf (20)

Probability
Probability Probability
Probability
 
Conditional-Probability-Powerpoint.pptx
Conditional-Probability-Powerpoint.pptxConditional-Probability-Powerpoint.pptx
Conditional-Probability-Powerpoint.pptx
 
T3 probability
T3   probabilityT3   probability
T3 probability
 
5. probability qt 1st tri semester
5. probability qt 1st tri semester 5. probability qt 1st tri semester
5. probability qt 1st tri semester
 
MATHS_PROBALITY_CIA_SEM-2[1].pptx
MATHS_PROBALITY_CIA_SEM-2[1].pptxMATHS_PROBALITY_CIA_SEM-2[1].pptx
MATHS_PROBALITY_CIA_SEM-2[1].pptx
 
Probability basics
Probability   basicsProbability   basics
Probability basics
 
Chapter 3 SE 2015.pptx
Chapter 3 SE 2015.pptxChapter 3 SE 2015.pptx
Chapter 3 SE 2015.pptx
 
Venn conditional
Venn conditionalVenn conditional
Venn conditional
 
Probability Theory 7
Probability Theory 7Probability Theory 7
Probability Theory 7
 
Applied statistics and probability for engineers solution montgomery && runger
Applied statistics and probability for engineers solution   montgomery && rungerApplied statistics and probability for engineers solution   montgomery && runger
Applied statistics and probability for engineers solution montgomery && runger
 
Boolean alg1a
Boolean alg1aBoolean alg1a
Boolean alg1a
 
R4 m.s. radhakrishnan, probability & statistics, dlpd notes.
R4 m.s. radhakrishnan, probability & statistics, dlpd notes.R4 m.s. radhakrishnan, probability & statistics, dlpd notes.
R4 m.s. radhakrishnan, probability & statistics, dlpd notes.
 
Introduction of Probability
Introduction of ProbabilityIntroduction of Probability
Introduction of Probability
 
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
 
Probability_Mutually exclusive event.ppt
Probability_Mutually exclusive event.pptProbability_Mutually exclusive event.ppt
Probability_Mutually exclusive event.ppt
 
Q&a probability
Q&a probabilityQ&a probability
Q&a probability
 
Chapter 4-Probability-2ndpart.ppt
Chapter 4-Probability-2ndpart.pptChapter 4-Probability-2ndpart.ppt
Chapter 4-Probability-2ndpart.ppt
 
Probability(not mutually exclusive events)
Probability(not mutually exclusive events)Probability(not mutually exclusive events)
Probability(not mutually exclusive events)
 
Information Theory and coding - Lecture 1
Information Theory and coding - Lecture 1Information Theory and coding - Lecture 1
Information Theory and coding - Lecture 1
 
Probability-04.pdf
Probability-04.pdfProbability-04.pdf
Probability-04.pdf
 

Recently uploaded

Hybridoma Technology ( Production , Purification , and Application )
Hybridoma Technology  ( Production , Purification , and Application  ) Hybridoma Technology  ( Production , Purification , and Application  )
Hybridoma Technology ( Production , Purification , and Application ) Sakshi Ghasle
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxheathfieldcps1
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxOH TEIK BIN
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxGaneshChakor2
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13Steve Thomason
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionSafetyChain Software
 
Micromeritics - Fundamental and Derived Properties of Powders
Micromeritics - Fundamental and Derived Properties of PowdersMicromeritics - Fundamental and Derived Properties of Powders
Micromeritics - Fundamental and Derived Properties of PowdersChitralekhaTherkar
 
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsPresiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsanshu789521
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAssociation for Project Management
 
URLs and Routing in the Odoo 17 Website App
URLs and Routing in the Odoo 17 Website AppURLs and Routing in the Odoo 17 Website App
URLs and Routing in the Odoo 17 Website AppCeline George
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxpboyjonauth
 
Alper Gobel In Media Res Media Component
Alper Gobel In Media Res Media ComponentAlper Gobel In Media Res Media Component
Alper Gobel In Media Res Media ComponentInMediaRes1
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptxVS Mahajan Coaching Centre
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeThiyagu K
 
_Math 4-Q4 Week 5.pptx Steps in Collecting Data
_Math 4-Q4 Week 5.pptx Steps in Collecting Data_Math 4-Q4 Week 5.pptx Steps in Collecting Data
_Math 4-Q4 Week 5.pptx Steps in Collecting DataJhengPantaleon
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactdawncurless
 

Recently uploaded (20)

Hybridoma Technology ( Production , Purification , and Application )
Hybridoma Technology  ( Production , Purification , and Application  ) Hybridoma Technology  ( Production , Purification , and Application  )
Hybridoma Technology ( Production , Purification , and Application )
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptx
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptx
 
Staff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSDStaff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSD
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory Inspection
 
Micromeritics - Fundamental and Derived Properties of Powders
Micromeritics - Fundamental and Derived Properties of PowdersMicromeritics - Fundamental and Derived Properties of Powders
Micromeritics - Fundamental and Derived Properties of Powders
 
Model Call Girl in Bikash Puri Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Bikash Puri  Delhi reach out to us at 🔝9953056974🔝Model Call Girl in Bikash Puri  Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Bikash Puri Delhi reach out to us at 🔝9953056974🔝
 
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsPresiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha elections
 
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdfTataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across Sectors
 
URLs and Routing in the Odoo 17 Website App
URLs and Routing in the Odoo 17 Website AppURLs and Routing in the Odoo 17 Website App
URLs and Routing in the Odoo 17 Website App
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptx
 
Alper Gobel In Media Res Media Component
Alper Gobel In Media Res Media ComponentAlper Gobel In Media Res Media Component
Alper Gobel In Media Res Media Component
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
_Math 4-Q4 Week 5.pptx Steps in Collecting Data
_Math 4-Q4 Week 5.pptx Steps in Collecting Data_Math 4-Q4 Week 5.pptx Steps in Collecting Data
_Math 4-Q4 Week 5.pptx Steps in Collecting Data
 
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impact
 

Probability-08.pdf

  • 2. MULTIPLICATIVE LAW FOR DEPENDENT EVENTS If A and B are two dependent events in a sample spaces S , then the probability that they will both occur is equal to the probability that one of the events will occur multiplied by the conditional probability that other event will occur given that one event has already occurred. P(A  B) = P(A)P(B/A) Provided P(A) 0 P(B  A) = P(B)P(A/B) Provided P(B) 0 Number of outcomes in (A  B) = m3 Therefore, P(A) = m1 n P(B) = m2 n P(A  B) = m3 n Let there are n equally likely outcomes in a sample space S. Number of outcomes in event A = m1 S m1 A  B A B m2 m3 n Number of outcome in event B = m2 Take P(A  B) = m3 n and multiply and divide on R.H.S by m1, we have P(A  B) = m3 n × m1 m1 = m1 n × m3 m1 The ratio m3/m1 is the conditional probability of event B given A has occurred, that is, P(B/A). Thus P(A  B) = P(A) P(B/A) Take P(A  B) = m3 n and multiply and divide on R.H.S by m2, we have P(A  B) = m3 n × m2 m2 = m2 n × m3 m2 The ratio m3/m2 is the conditional probability of event A given B has occurred, that is, P(A/B) Thus P(A  B) = P(B) P(A/B) Amjad Ali 2
  • 3. PROBLEM 48 A bag contains four red and three black balls. Find the probability that a red ball is drawn in the first draw and a black ball in the second in two consecutive draws, without replacement from the bag. Red Black Total 4 3 7 A: Red ball on first draw P(A) = 4/7 B: Black ball on second draw P(B/A) = 3/6 P(A  B) = P(A)P(B/A) P(A  B) = (4/7)(3/6) P(A  B) = 2/7 Red Black Total 4 3 7 1st draw 1 0 3 3 6 Amjad Ali 3
  • 4. PROBLEM 49 Find the probability of drawing a face (pictured) card on each of two consecutive draws from a standard pack, without replacement of the first card. Face cards Rest cards Total 12 40 52 A: Selecting a pictured card on first draw P(A) = 12/52 B: Selecting a pictured card on second draw P(B/A) = 11/51 P(A  B) = P(A)P(B/A) P(A  B) = (12/52)(11/51) P(A  B) = 11/221 Face Rest Total 12 40 52 1st draw 1 0 11 40 51 Amjad Ali 4
  • 5. PROBLEM 50 A box contains 15 items, 4 of which are defective and 11 are good. Two items are selected. What is the probability that the first is good and second is defective? Defective Good Total 4 11 15 A: First is good P(A) = 11/15 B: Second is defective P(B/A) = 4/14 P(A  B) = P(A)P(B/A) P(A  B) = (11/15)(4/14) P(A  B) = 22/105 Defective Good Total 4 11 15 1st item 0 1 4 10 14 Amjad Ali 5
  • 6. PROBLEM 51 Two balls are drawn in succession without replacement from a bag containing 5 white and 8 black balls. What is the probability that both balls are black? White Black Total 5 8 13 A: Black ball on first draw P(A) = 8/13 B: Black ball on second draw P(B/A) = 7/12 P(A  B) = P(A)P(B/A) P(A  B) = (8/13)(7/12) P(A  B) = 14/39 White Black Total 5 8 13 1st draw 0 1 5 7 12 Amjad Ali 6
  • 7. PROBLEM 52 Two drawing each of three balls is made from a bag containing 5 white and 8 black balls; the balls are not being replaced before the next trial. What is the probability that the first drawing will give 3 white balls and the second 3 black balls? White Black Total 5 8 13 A: 3 white balls on first draw 143 5 C C . C P(A) 3 13 0 8 3 5   White Black Total 5 8 13 1st draw 3 0 2 8 10 2nd draw 0 3 B: 3 black balls on second draw 15 7 C C . C P(B/A) 3 10 3 8 0 2   P(B/A) . P(A) B) P(A   429 7 15 7 . 143 5 B) P(A    Amjad Ali 7
  • 8. PROBLEM 53 Show that the multiplication law P(AB) = P(A) P(B/ A), established for two events, may be generalized to three events as follows; P (A  B  C) = P (A) P (B/ A) P (C/ A  B) Let A  B = K Then A  B  C = K  C P(A  B  C) = P(K  C) = P(K ) P(C/ K) = P(A  B ) P(C/ K) = P(A ) P(B/A ) P(C/ A  B) PROBLEM 54 Show that the multiplication law P(A  B) = P(B) P(A/ B), established for two events, may be generalized to three events as follows; P (A  B  C) = P (A/ B  C) P (B/ C) P ( C ) Let B  C = K Then A  B  C = A  K P(A  B  C) = P(A  K) = P(K ) P(A/ K) = P(B  C ) P(A/ K) = P(C ) P(B/C ) P(A/ B  C) = P(A/ B  C) P(B/C ) P(C ) Amjad Ali 8
  • 9. PROBLEM 55 A farmer has a box containing 30 eggs, five of which have blood spots, he checks eggs by taking them at random one after another from the box what is the probability that the first two eggs have spots and third will be clear. Spotted Clear Total 5 25 30 A: First is blood spotted P(A) = 5/30 A: Second is blood spotted P(B/A) = 4/29 C: Third is clear (good) P(C/ A  B) = 25/28 P(A  B  C) = P(A) P(B/A) P(C/A  B) P(A  B  C) = (5/30) (4/29) (25/28) = 25/1218 Spotted Clear Total 5 25 30 First 1 0 4 25 29 Second 1 0 3 25 28 Third 0 1 Amjad Ali 9
  • 10. PROBLEM 56 Three cards are drawn in succession without replacement from an ordinary deck of playing cards. Find the probability that the first card is a red ace, the second card is a ten or jack, and the third card is greater than three but less than seven. A: First is a red ace P(A) = 2/52 B: Second is a ten or jack P(B/A) = 8/51 C: Third is greater than 3 but less than 7 P(C/A  B) = 12/50 P(A  B  C) = P(A) P(B/A) P(C/A  B) P(A  B  C) = (2/52) (8/51) (12/50) = 8/5525 Amjad Ali 10