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Binomial Probability Distribution 

Suppose that we have an experiment such as tossing a coin or die repeatedly or choosing a marble from an urn repeatedly. Each toss or selection is called a trial. In any single trial there will be a probability associated with a particular event such as head on the coin, 4 on the die, or selection of a red marble. In some cases this probability will not change from one trial to the next (as in tossing a coin or die.) Such trials are then said to be independent and are often called Bernoulli trials after James Bernoulli who investigated them at the end of the seventeenth century.

Let p be the probability that an event will happen in any single Bernoulli trial (called the probability of success). Then q = 1 - p is the probability that the event will fail to happen in any single trial (called the probability of failure). The probability that the event will happen exactly x times in n trials (i.e., n successes and n - x failures will occur) is given by the probability function.

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

where the random variable X denotes the number of successes in n trials and x = 0, 1, ........n.

Example : The probability of getting exactly 2 heads in 6 tosses of a fair coin is

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

The discrete probability function (i) is often called the binomial distribution since for x = 0, 1, 2, .........n, it corresponds to successive terms in the binomial expansion

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

The special case of a binomial distribution with n = 1 is also called the Bernoulli distribution.

 

Ex.1 If a fair coin is tossed 10 times, find the probability of

(i) exactly six heads 

(ii) atleast six heads 

(iii) atmost six heads

Sol. The repeated tosses of a coin are Bernoulli trials. Let X denotes the number of heads in an experiment of 10 trials. Clearly, X has the binomial distribution with n = 10 and p = 1/2

Therefore P(X = x) = nCxqn–x px, x = 0, 1, 2, ........n

Here n = 10, p =1/2, q=1=1-p = 1/2. 

 Therefore P(X = x) =  Binomial Distribution | Mathematics (Maths) Class 12 - JEE

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

(ii) P (atleast six heads) = P (X ≥ 6) = P (X = 6) + P (X = 7) + P(X = 8) + P (X = 9) + P(X = 10)

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

(iii) P (at most six heads) = P (X ≤ 6)

= P(X = 0) + P(x = 1) + P(X = 2) + P(X = 3) + P(X = 4) + P(X = 5) + P(X = 6)

Binomial Distribution | Mathematics (Maths) Class 12 - JEE  Binomial Distribution | Mathematics (Maths) Class 12 - JEE

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

 

Ex.2 A coin is tossed 7 times. Each time a man calls head. The probability that he wins the toss on more than three occasions is

Sol. The man has to win at least 4 times then required probability

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

 

Ex.3 A man takes a step forward with probability 0.4 and backward with probability 0.6. Find the probability that at the end of eleven steps he is one step away from the starting point.

Sol. Since the man is one step away from starting point mean that either

(i) man has taken 6 steps forward and 5 steps backward.

(ii) man has taken 5 steps forward and 6 steps backward.

Taking, movement 1 step forward as success and 1 step backward as failure.

p = Probability of success = 0.4 and q = Probability of failure = 0.6

 Required Probability = P [X = 6 or X = 5] = P [X = 6] + P(X = 5)  = 11C6p6 q5 +11 C5p5 q6

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

Binomial Distribution | Mathematics (Maths) Class 12 - JEE

Hence the required probability = 0.37

 

The document Binomial Distribution | Mathematics (Maths) Class 12 - JEE is a part of the JEE Course Mathematics (Maths) Class 12.
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FAQs on Binomial Distribution - Mathematics (Maths) Class 12 - JEE

1. What is the binomial distribution?
Ans. The binomial distribution is a probability distribution that describes the number of successes in a fixed number of independent Bernoulli trials, where each trial has the same probability of success.
2. How is the binomial distribution different from other probability distributions?
Ans. The binomial distribution is specific to situations where there are only two possible outcomes (success or failure) and a fixed number of trials. Other probability distributions, such as the normal distribution or Poisson distribution, can describe a wider range of scenarios.
3. How can the binomial distribution be applied in real-life situations?
Ans. The binomial distribution can be applied to various real-life situations, such as predicting the number of defective products in a manufacturing process, the number of successful sales calls in a day, or the number of students passing an exam.
4. What are the key parameters of the binomial distribution?
Ans. The key parameters of the binomial distribution are the number of trials (n) and the probability of success in each trial (p). These parameters determine the shape and characteristics of the distribution.
5. How is the binomial distribution calculated?
Ans. The probability mass function (PMF) of the binomial distribution can be calculated using the formula P(X = k) = C(n, k) * p^k * (1-p)^(n-k), where X is the random variable representing the number of successes, n is the number of trials, k is the number of successes, p is the probability of success, and C(n, k) is the binomial coefficient.
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