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Probability
561 – Sachinkumar Patel
562 – Prakash Prasad
663 – Shreya Rana
564 – Pratham Raut
565 – Srushti Redkar
Probability
 How likely something is to happen.
 All probabilities are between 0 and 1.
 Event
 Simple Event
 Sample Space
 Formula =
 P(A) = n(A)/n(S)
History of probability
 17th century
 By Pierre de Fermat and Blaise Pascal.
Conditional Probability
 If A and B are two events associated with the same sample space of
a random experiment, the conditional probability of event A given
that B has occurred is given by p(A/B)=P( A ∩ B ) / P( B ), provided
P( B ) ≠ 0.
 Formula =
 P(A | B) = P(A ∩ B) / P(B) ( P(B) ≠ 0 )
 P(B | A) = P(A ∩ B) / P(A) ( P(A) ≠ 0 )
Independent Events and Dependent Events
 Independent Event
- The outcome of one event does
not affect the outcome of the
other event.
- Formula =
- P( A ∩ B ) = P( A ) * P( B )
 Dependent Event
- The outcome of one event does
influence the outcome of the other
event.
- Formula =
- P( A ∩ B ) = P( A ) * P( B after A )
Mutually exclusive events
 The events are mutually exclusive if they don’t occur same time or
simultaneously.
 Formula =
 P (A ∪ B) = P(A) + P(B)
Probability Distribution
 Random Variable = X
- A type of variable in statistics whose possible values depend on the
outcomes of a certain random phenomenon.
• Probability Distribution = P(X)
- If a random variable x takes values x1, x2, …., xn with respective
probability p1, p2, …., pn, then is known as the probability distribution of
X.
 Formula =
 𝑖=1
𝑛
𝑝𝑖 = 1
X X1 X2 …….. Xn
P(X) P1 P2 …….. Pn
Sums 561
R Implementation 561
Sum 562
R Implementation 562
Sum 563
R Implementation 563
Sum 564
R Implementation 564
Sum 565
R Implementation 565
Application of probability
 Flipping a coin
 Choosing a card from the deck
 Throwing a dice
 Pulling a green candy from a bag of red candies
 Winning a lottery 1 in many millions
 Weather forecasting
 Calculation of batting average in cricket
THANK YOU

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Probability.pptx

  • 1. Probability 561 – Sachinkumar Patel 562 – Prakash Prasad 663 – Shreya Rana 564 – Pratham Raut 565 – Srushti Redkar
  • 2. Probability  How likely something is to happen.  All probabilities are between 0 and 1.  Event  Simple Event  Sample Space  Formula =  P(A) = n(A)/n(S)
  • 3. History of probability  17th century  By Pierre de Fermat and Blaise Pascal.
  • 4. Conditional Probability  If A and B are two events associated with the same sample space of a random experiment, the conditional probability of event A given that B has occurred is given by p(A/B)=P( A ∩ B ) / P( B ), provided P( B ) ≠ 0.  Formula =  P(A | B) = P(A ∩ B) / P(B) ( P(B) ≠ 0 )  P(B | A) = P(A ∩ B) / P(A) ( P(A) ≠ 0 )
  • 5. Independent Events and Dependent Events  Independent Event - The outcome of one event does not affect the outcome of the other event. - Formula = - P( A ∩ B ) = P( A ) * P( B )  Dependent Event - The outcome of one event does influence the outcome of the other event. - Formula = - P( A ∩ B ) = P( A ) * P( B after A )
  • 6. Mutually exclusive events  The events are mutually exclusive if they don’t occur same time or simultaneously.  Formula =  P (A ∪ B) = P(A) + P(B)
  • 7. Probability Distribution  Random Variable = X - A type of variable in statistics whose possible values depend on the outcomes of a certain random phenomenon. • Probability Distribution = P(X) - If a random variable x takes values x1, x2, …., xn with respective probability p1, p2, …., pn, then is known as the probability distribution of X.  Formula =  𝑖=1 𝑛 𝑝𝑖 = 1 X X1 X2 …….. Xn P(X) P1 P2 …….. Pn
  • 18. Application of probability  Flipping a coin  Choosing a card from the deck  Throwing a dice  Pulling a green candy from a bag of red candies  Winning a lottery 1 in many millions  Weather forecasting  Calculation of batting average in cricket