SlideShare a Scribd company logo
What is the probability that the sky will fall?
     Write your answer as a fraction.
     Do not discuss with a neighbour.




     An Introduction to Probability
  http://mathforum.org/dr.math/faq/faq.prob.intro.html


                                              Chicken Little
Some vocabulary ...

    Probability: the branch of mathematics that deals with chance.

    Sample Space (Ω): the set of all possible outcomes for a given
    quot;Experimentquot; represented by capital omega Ω.

    Event (E): A subset of the sample space. A particular occurance in a given
    experiment.

    Simple Event: The result of an experiment that is carried out in a single
    step.

    Example: Flip a coin. The result is heads
    (a simple event)

    Compound Event: The result of an experiment carried out in two (or
    more) steps.

    Example: 1. Flip a coin and roll a die - 6 .The result is {H,6}

    Example 2: Flip a coin twice. The result is {H,T}
Calculating the Probability of Event A



                                       number of favourable outcomes




                                       sample space




Probability can be expressed as:
- a Ratio
- a Fraction                 IMPORTANT: Probability of any event
- a Decimal                  is always a number between 0 and 1.
- a Percent
Certain Event: an event whose probability is 1

Example: roll a die - 6 and get a result less than 10

Impossible Event: an event whose probability is 0.

Example: roll a die - 6 and get a 7

Complimentary Event: the compliment of E is E':

                      If P(E) = a then P(E') = 1-a

Example: Given a standard deck of cards, a card is drawn at random.

P(spade) = (13/52) = (1/4)

P (not a spade) = 1 - (1/4) = (3/4)
Determine the sample space when a fair die is rolled once.

 Solution: It is possible to roll a 1, 2, 3, 4, 5 or 6. This is the sample space.


Determine the sample space for rolling a six sided die and flipping a coin.
Determine the probability of rolling a 2 when rolling a fair die.



Determine the probability of getting a head and an odd number when rolling
a die and flipping a coin.
A bus is scheduled to arrive at a train station at any time between 07:05 and
07:15 inclusive. A train is scheduled to arrive between 07:11 and 07:17
inclusive. The arrival of a bus at 7:06 and a train at 07:14 can be represented
by the ordered pair (6, 14). Times are expressed in whole minutes.
a) Sketch a sample space for this event (use a chart).
b) How many ordered pairs are there in this sample space?
c) How many ordered pairs have the bus and the train arriving at the same time?
d) How many ordered pairs have the train arriving after the bus?
e) What is the probability of the bus arriving after the train?
Pre-Cal 40S Slides December 21, 2007

More Related Content

What's hot

Percent
PercentPercent
Percent
Mohd Shanu
 
23. induction consecutiveints cas_touchpad
23. induction consecutiveints cas_touchpad23. induction consecutiveints cas_touchpad
23. induction consecutiveints cas_touchpad
Media4math
 
Probability
ProbabilityProbability
Probability
Nikhil Gupta
 
Probability class 9 ____ CBSE
Probability class 9 ____ CBSEProbability class 9 ____ CBSE
Probability class 9 ____ CBSE
Smrithi Jaya
 
probability ,and its terminologies full description
probability ,and its terminologies full descriptionprobability ,and its terminologies full description
probability ,and its terminologies full description
AMRITGUPTA2345
 
Lecture: Monte Carlo Methods
Lecture: Monte Carlo MethodsLecture: Monte Carlo Methods
Lecture: Monte Carlo Methods
Frank Kienle
 
Probabilty of Events
Probabilty of EventsProbabilty of Events
Probabilty of Events
Thembile Tungane
 
Monte carlo simulation
Monte carlo simulationMonte carlo simulation
Monte carlo simulation
Anurag Jaiswal
 
Probability
ProbabilityProbability
Probability
Rushina Singhi
 
STAT: Random experiments(2)
STAT: Random experiments(2)STAT: Random experiments(2)
STAT: Random experiments(2)
Tuenti SiIx
 

What's hot (10)

Percent
PercentPercent
Percent
 
23. induction consecutiveints cas_touchpad
23. induction consecutiveints cas_touchpad23. induction consecutiveints cas_touchpad
23. induction consecutiveints cas_touchpad
 
Probability
ProbabilityProbability
Probability
 
Probability class 9 ____ CBSE
Probability class 9 ____ CBSEProbability class 9 ____ CBSE
Probability class 9 ____ CBSE
 
probability ,and its terminologies full description
probability ,and its terminologies full descriptionprobability ,and its terminologies full description
probability ,and its terminologies full description
 
Lecture: Monte Carlo Methods
Lecture: Monte Carlo MethodsLecture: Monte Carlo Methods
Lecture: Monte Carlo Methods
 
Probabilty of Events
Probabilty of EventsProbabilty of Events
Probabilty of Events
 
Monte carlo simulation
Monte carlo simulationMonte carlo simulation
Monte carlo simulation
 
Probability
ProbabilityProbability
Probability
 
STAT: Random experiments(2)
STAT: Random experiments(2)STAT: Random experiments(2)
STAT: Random experiments(2)
 

Similar to Pre-Cal 40S Slides December 21, 2007

STAB52 Lecture Notes (Week 2)
STAB52 Lecture Notes (Week 2)STAB52 Lecture Notes (Week 2)
STAB52 Lecture Notes (Week 2)
Danny Cao
 
Probability[1]
Probability[1]Probability[1]
Probability[1]
indu thakur
 
Probability.pdf
Probability.pdfProbability.pdf
Probability.pdf
Shivakumar B N
 
Probability By Ms Aarti
Probability By Ms AartiProbability By Ms Aarti
Probability By Ms Aarti
kulachihansraj
 
Nossi ch 10
Nossi ch 10Nossi ch 10
Nossi ch 10
lesaturner
 
Indefinite integration class 12
Indefinite integration class 12Indefinite integration class 12
Indefinite integration class 12
nysa tutorial
 
vinayjoshi-131204045346-phpapp02.pdf
vinayjoshi-131204045346-phpapp02.pdfvinayjoshi-131204045346-phpapp02.pdf
vinayjoshi-131204045346-phpapp02.pdf
sanjayjha933861
 
Recap_Of_Probability.pptx
Recap_Of_Probability.pptxRecap_Of_Probability.pptx
Recap_Of_Probability.pptx
ssuseree099d2
 
PROBABILITY
PROBABILITYPROBABILITY
PROBABILITY
VIV13
 
4.1-4.2 Sample Spaces and Probability
4.1-4.2 Sample Spaces and Probability4.1-4.2 Sample Spaces and Probability
4.1-4.2 Sample Spaces and Probability
mlong24
 
Probablity ppt maths
Probablity ppt mathsProbablity ppt maths
Probablity ppt maths
neelkanth ramteke
 
PROBABILITY.pptx
PROBABILITY.pptxPROBABILITY.pptx
PROBABILITY.pptx
GeeyaMarielAntonio
 
GRADE 10 MATH Probability and Statistics
GRADE 10 MATH Probability and StatisticsGRADE 10 MATH Probability and Statistics
GRADE 10 MATH Probability and Statistics
kenthromulo
 
SAMPLE SPACES and PROBABILITY (3).pptx
SAMPLE SPACES and PROBABILITY (3).pptxSAMPLE SPACES and PROBABILITY (3).pptx
SAMPLE SPACES and PROBABILITY (3).pptx
victormiralles2
 
Rishabh sehrawat probability
Rishabh sehrawat probabilityRishabh sehrawat probability
Rishabh sehrawat probability
Rishabh Sehrawat
 
Probability
ProbabilityProbability
Probability
Anjali Devi J S
 
02 math essentials
02 math essentials02 math essentials
02 math essentials
Poongodi Mano
 
introduction to Probability theory
introduction to Probability theoryintroduction to Probability theory
introduction to Probability theory
Rachna Gupta
 
Probability PART 1 - X NCERT
Probability PART 1 - X NCERTProbability PART 1 - X NCERT
Probability PART 1 - X NCERT
SudheerVenkat2
 
Probability..
Probability..Probability..
Probability..
SAYALI ZENDE
 

Similar to Pre-Cal 40S Slides December 21, 2007 (20)

STAB52 Lecture Notes (Week 2)
STAB52 Lecture Notes (Week 2)STAB52 Lecture Notes (Week 2)
STAB52 Lecture Notes (Week 2)
 
Probability[1]
Probability[1]Probability[1]
Probability[1]
 
Probability.pdf
Probability.pdfProbability.pdf
Probability.pdf
 
Probability By Ms Aarti
Probability By Ms AartiProbability By Ms Aarti
Probability By Ms Aarti
 
Nossi ch 10
Nossi ch 10Nossi ch 10
Nossi ch 10
 
Indefinite integration class 12
Indefinite integration class 12Indefinite integration class 12
Indefinite integration class 12
 
vinayjoshi-131204045346-phpapp02.pdf
vinayjoshi-131204045346-phpapp02.pdfvinayjoshi-131204045346-phpapp02.pdf
vinayjoshi-131204045346-phpapp02.pdf
 
Recap_Of_Probability.pptx
Recap_Of_Probability.pptxRecap_Of_Probability.pptx
Recap_Of_Probability.pptx
 
PROBABILITY
PROBABILITYPROBABILITY
PROBABILITY
 
4.1-4.2 Sample Spaces and Probability
4.1-4.2 Sample Spaces and Probability4.1-4.2 Sample Spaces and Probability
4.1-4.2 Sample Spaces and Probability
 
Probablity ppt maths
Probablity ppt mathsProbablity ppt maths
Probablity ppt maths
 
PROBABILITY.pptx
PROBABILITY.pptxPROBABILITY.pptx
PROBABILITY.pptx
 
GRADE 10 MATH Probability and Statistics
GRADE 10 MATH Probability and StatisticsGRADE 10 MATH Probability and Statistics
GRADE 10 MATH Probability and Statistics
 
SAMPLE SPACES and PROBABILITY (3).pptx
SAMPLE SPACES and PROBABILITY (3).pptxSAMPLE SPACES and PROBABILITY (3).pptx
SAMPLE SPACES and PROBABILITY (3).pptx
 
Rishabh sehrawat probability
Rishabh sehrawat probabilityRishabh sehrawat probability
Rishabh sehrawat probability
 
Probability
ProbabilityProbability
Probability
 
02 math essentials
02 math essentials02 math essentials
02 math essentials
 
introduction to Probability theory
introduction to Probability theoryintroduction to Probability theory
introduction to Probability theory
 
Probability PART 1 - X NCERT
Probability PART 1 - X NCERTProbability PART 1 - X NCERT
Probability PART 1 - X NCERT
 
Probability..
Probability..Probability..
Probability..
 

More from Darren Kuropatwa

Behind Their Eyes v1
Behind Their Eyes v1Behind Their Eyes v1
Behind Their Eyes v1
Darren Kuropatwa
 
Leading Change v1
Leading Change v1Leading Change v1
Leading Change v1
Darren Kuropatwa
 
Providing Permission To Wonder v2.1
Providing Permission To Wonder v2.1Providing Permission To Wonder v2.1
Providing Permission To Wonder v2.1
Darren Kuropatwa
 
Things That Suck About Digital Citizenship v1
Things That Suck About Digital Citizenship v1Things That Suck About Digital Citizenship v1
Things That Suck About Digital Citizenship v1
Darren Kuropatwa
 
Digital Storytelling for Deeper Learning v1
Digital Storytelling for Deeper Learning v1Digital Storytelling for Deeper Learning v1
Digital Storytelling for Deeper Learning v1
Darren Kuropatwa
 
Tales of Learning and the Gifts of Footprints v4.2
Tales of Learning and the Gifts of Footprints v4.2Tales of Learning and the Gifts of Footprints v4.2
Tales of Learning and the Gifts of Footprints v4.2
Darren Kuropatwa
 
The Fourth Screen v4.2
The Fourth Screen v4.2The Fourth Screen v4.2
The Fourth Screen v4.2
Darren Kuropatwa
 
Making Student Thinking Visible v4
 Making Student Thinking Visible v4 Making Student Thinking Visible v4
Making Student Thinking Visible v4
Darren Kuropatwa
 
Learning is at BYTE 2017
Learning is at BYTE 2017Learning is at BYTE 2017
Learning is at BYTE 2017
Darren Kuropatwa
 
Leveraging Digital for Deeper Learning
Leveraging Digital for Deeper LearningLeveraging Digital for Deeper Learning
Leveraging Digital for Deeper Learning
Darren Kuropatwa
 
We Learn Through Stories at PRIZMAH17
We Learn Through Stories at PRIZMAH17We Learn Through Stories at PRIZMAH17
We Learn Through Stories at PRIZMAH17
Darren Kuropatwa
 
Providing Permission to Wonder v3
Providing Permission to Wonder v3Providing Permission to Wonder v3
Providing Permission to Wonder v3
Darren Kuropatwa
 
The Fourth Screen v4.1
The Fourth Screen v4.1The Fourth Screen v4.1
The Fourth Screen v4.1
Darren Kuropatwa
 
Making Student Thinking Visible v3.7
 Making Student Thinking Visible v3.7 Making Student Thinking Visible v3.7
Making Student Thinking Visible v3.7
Darren Kuropatwa
 
Learning is at AUHSD
Learning is at AUHSDLearning is at AUHSD
Learning is at AUHSD
Darren Kuropatwa
 
Providing Permission to Wonder v2
Providing Permission to Wonder v2Providing Permission to Wonder v2
Providing Permission to Wonder v2
Darren Kuropatwa
 
The Fourth Screen v4
The Fourth Screen v4The Fourth Screen v4
The Fourth Screen v4
Darren Kuropatwa
 
Learning is at BLC16
Learning is at BLC16Learning is at BLC16
Learning is at BLC16
Darren Kuropatwa
 
We Learn Through Stories v4 (master class)
We Learn Through Stories v4 (master class)We Learn Through Stories v4 (master class)
We Learn Through Stories v4 (master class)
Darren Kuropatwa
 
Deep Learning Design: the middle ring
Deep Learning Design: the middle ringDeep Learning Design: the middle ring
Deep Learning Design: the middle ring
Darren Kuropatwa
 

More from Darren Kuropatwa (20)

Behind Their Eyes v1
Behind Their Eyes v1Behind Their Eyes v1
Behind Their Eyes v1
 
Leading Change v1
Leading Change v1Leading Change v1
Leading Change v1
 
Providing Permission To Wonder v2.1
Providing Permission To Wonder v2.1Providing Permission To Wonder v2.1
Providing Permission To Wonder v2.1
 
Things That Suck About Digital Citizenship v1
Things That Suck About Digital Citizenship v1Things That Suck About Digital Citizenship v1
Things That Suck About Digital Citizenship v1
 
Digital Storytelling for Deeper Learning v1
Digital Storytelling for Deeper Learning v1Digital Storytelling for Deeper Learning v1
Digital Storytelling for Deeper Learning v1
 
Tales of Learning and the Gifts of Footprints v4.2
Tales of Learning and the Gifts of Footprints v4.2Tales of Learning and the Gifts of Footprints v4.2
Tales of Learning and the Gifts of Footprints v4.2
 
The Fourth Screen v4.2
The Fourth Screen v4.2The Fourth Screen v4.2
The Fourth Screen v4.2
 
Making Student Thinking Visible v4
 Making Student Thinking Visible v4 Making Student Thinking Visible v4
Making Student Thinking Visible v4
 
Learning is at BYTE 2017
Learning is at BYTE 2017Learning is at BYTE 2017
Learning is at BYTE 2017
 
Leveraging Digital for Deeper Learning
Leveraging Digital for Deeper LearningLeveraging Digital for Deeper Learning
Leveraging Digital for Deeper Learning
 
We Learn Through Stories at PRIZMAH17
We Learn Through Stories at PRIZMAH17We Learn Through Stories at PRIZMAH17
We Learn Through Stories at PRIZMAH17
 
Providing Permission to Wonder v3
Providing Permission to Wonder v3Providing Permission to Wonder v3
Providing Permission to Wonder v3
 
The Fourth Screen v4.1
The Fourth Screen v4.1The Fourth Screen v4.1
The Fourth Screen v4.1
 
Making Student Thinking Visible v3.7
 Making Student Thinking Visible v3.7 Making Student Thinking Visible v3.7
Making Student Thinking Visible v3.7
 
Learning is at AUHSD
Learning is at AUHSDLearning is at AUHSD
Learning is at AUHSD
 
Providing Permission to Wonder v2
Providing Permission to Wonder v2Providing Permission to Wonder v2
Providing Permission to Wonder v2
 
The Fourth Screen v4
The Fourth Screen v4The Fourth Screen v4
The Fourth Screen v4
 
Learning is at BLC16
Learning is at BLC16Learning is at BLC16
Learning is at BLC16
 
We Learn Through Stories v4 (master class)
We Learn Through Stories v4 (master class)We Learn Through Stories v4 (master class)
We Learn Through Stories v4 (master class)
 
Deep Learning Design: the middle ring
Deep Learning Design: the middle ringDeep Learning Design: the middle ring
Deep Learning Design: the middle ring
 

Recently uploaded

Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with SlackLet's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
shyamraj55
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Safe Software
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Neo4j
 
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceAI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
IndexBug
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Zilliz
 
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalizationFull-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Zilliz
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
Claudio Di Ciccio
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Safe Software
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
innovationoecd
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Kari Kakkonen
 
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
DianaGray10
 
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AIMind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
Kumud Singh
 
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI modelsInfrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
Zilliz
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Tomaz Bratanic
 
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Edge AI and Vision Alliance
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Matthew Sinclair
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Neo4j
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
Alpen-Adria-Universität
 
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Zilliz
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
SOFTTECHHUB
 

Recently uploaded (20)

Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with SlackLet's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
 
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceAI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
 
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalizationFull-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
 
“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”“I’m still / I’m still / Chaining from the Block”
“I’m still / I’m still / Chaining from the Block”
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
 
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
 
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AIMind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
 
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI modelsInfrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
 
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
 
Video Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the FutureVideo Streaming: Then, Now, and in the Future
Video Streaming: Then, Now, and in the Future
 
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
 
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
 

Pre-Cal 40S Slides December 21, 2007

  • 1. What is the probability that the sky will fall? Write your answer as a fraction. Do not discuss with a neighbour. An Introduction to Probability http://mathforum.org/dr.math/faq/faq.prob.intro.html Chicken Little
  • 2. Some vocabulary ... Probability: the branch of mathematics that deals with chance. Sample Space (Ω): the set of all possible outcomes for a given quot;Experimentquot; represented by capital omega Ω. Event (E): A subset of the sample space. A particular occurance in a given experiment. Simple Event: The result of an experiment that is carried out in a single step. Example: Flip a coin. The result is heads (a simple event) Compound Event: The result of an experiment carried out in two (or more) steps. Example: 1. Flip a coin and roll a die - 6 .The result is {H,6} Example 2: Flip a coin twice. The result is {H,T}
  • 3. Calculating the Probability of Event A number of favourable outcomes sample space Probability can be expressed as: - a Ratio - a Fraction IMPORTANT: Probability of any event - a Decimal is always a number between 0 and 1. - a Percent
  • 4. Certain Event: an event whose probability is 1 Example: roll a die - 6 and get a result less than 10 Impossible Event: an event whose probability is 0. Example: roll a die - 6 and get a 7 Complimentary Event: the compliment of E is E': If P(E) = a then P(E') = 1-a Example: Given a standard deck of cards, a card is drawn at random. P(spade) = (13/52) = (1/4) P (not a spade) = 1 - (1/4) = (3/4)
  • 5. Determine the sample space when a fair die is rolled once. Solution: It is possible to roll a 1, 2, 3, 4, 5 or 6. This is the sample space. Determine the sample space for rolling a six sided die and flipping a coin.
  • 6. Determine the probability of rolling a 2 when rolling a fair die. Determine the probability of getting a head and an odd number when rolling a die and flipping a coin.
  • 7. A bus is scheduled to arrive at a train station at any time between 07:05 and 07:15 inclusive. A train is scheduled to arrive between 07:11 and 07:17 inclusive. The arrival of a bus at 7:06 and a train at 07:14 can be represented by the ordered pair (6, 14). Times are expressed in whole minutes. a) Sketch a sample space for this event (use a chart). b) How many ordered pairs are there in this sample space? c) How many ordered pairs have the bus and the train arriving at the same time? d) How many ordered pairs have the train arriving after the bus? e) What is the probability of the bus arriving after the train?