Baye's Theorem
In the previous section, we have learnt that i) If A and B are two mutually exclusive events, then ii) Before we state and prove Baye's Theorem, we use the above two rules to state the law of total probability. The law of total probability is useful in proving Baye's theorem and..
In the previous section, we have learnt that i) If A and B are two mutually exclusive events, then ii) Before we state and prove Baye's Theorem, we use the above two rules to state the law of total probability. The law of total probability is useful in proving Baye's theorem and..Question 26
Question: In the EXIT POLL results given by a TV Channel in the past few years, it was observed that 65 out of 80 times, the predictions proved correct. What is the probability that the next EXIT POLL result given by the TV channel will be correct? What is the probability t..
Question: In the EXIT POLL results given by a TV Channel in the past few years, it was observed that 65 out of 80 times, the predictions proved correct. What is the probability that the next EXIT POLL result given by the TV channel will be correct? What is the probability t..Baye's Theorem Baye's Theorem - In the previous section, we have learnt that i) If A and B are two mutually exclusive events, then ii) Before we state and prove Baye's Theorem, we use the above two rules to state the law of total probability. The law of total probability is useful in proving B..
Baye's Theorem - In the previous section, we have learnt that i) If A and B are two mutually exclusive events, then ii) Before we state and prove Baye's Theorem, we use the above two rules to state the law of total probability. The law of total probability is useful in proving B..Introduction
In our day to day life, we come across many uncertainty of events. We wake up in the morning and check the weather report. The statement could be 'there is 60% chance of rain today'. This statement infers that the chance of rain is more than that having a dry weather. We decide upon our break..
Binomial Distribution
A trial, which has only two outcomes i.e., "a success" or "a failure", is called a Bernoulli trial . The probability distribution of the number of successes, so obtained is called the binomial distribution..
Calculation of Standard Deviation for discrete series
In the discrete series, when deviations are taken from actual mean, the formula ..
In the discrete series, when deviations are taken from actual mean, the formula ..Calculation of Standard Deviation Calculation of Standard Deviation for discrete series - In the discrete series, when deviations are taken from actual mean, the formula ..
Calculation of Standard Deviation for discrete series - In the discrete series, when deviations are taken from actual mean, the formula ..Remark 7:
So far, we have assumed that the elementary events are equally likely and we have used the corresponding definition of probability. However the same definition of conditional probability can also be used when the elementary events are not equally likely. This will be clear from..
So far, we have assumed that the elementary events are equally likely and we have used the corresponding definition of probability. However the same definition of conditional probability can also be used when the elementary events are not equally likely. This will be clear from..Question 28
Question: The marks of a student in 10 tests are given below: If 60% and above is I class, what is the probability that the student gets I class in any test? Answer: Total number of tests = 10 Number of tests in which the student gets I class = 6 Probability o..
Question: The marks of a student in 10 tests are given below: If 60% and above is I class, what is the probability that the student gets I class in any test? Answer: Total number of tests = 10 Number of tests in which the student gets I class = 6 Probability o..Poisson Distribution as a Limiting Form of the Binomial Distribution
where l is a finite number and is equal to np. The sum of the probabilities P(X = r) or simply P(r) for r = 0, 1, 2, is 1. This can be seen by putting r = 0, 1, 2, in (4) and adding all the probabilities. Also, each of the probabilities is a non-negative fraction. This..
where l is a finite number and is equal to np. The sum of the probabilities P(X = r) or simply P(r) for r = 0, 1, 2, is 1. This can be seen by putting r = 0, 1, 2, in (4) and adding all the probabilities. Also, each of the probabilities is a non-negative fraction. This..See what our Users say :
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