basic probability formula

Introduction - Statistics is about collection of information and its presentation and about drawing inferences from these. We come across facts and figures in the newspapers, Television and the radio. The numerical figures are called "the data". If we have to draw good inferences from information c..

Objective Function - The Objective Function is a linear function of variables which is to be optimised i.e., maximised or minimised. e.g., profit function, cost function etc. The objective function may be expressed as a linear expressio..

Applications - After the elementary study in probability, let us see how we can utilise this basic knowledge in solving problems of different are..

The graphical method of solving an LPP is possible only if there are two decision variables (say x and y). This method is not suitable if there are three or more decision variables. In this case, there is a powerful method called 'simplex method'. The wide usage of liner programming helps in busine..

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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 ..

Let A and B be any two events associated with a random experiment. The probability of occurrence of event A when the event B has already occurred is called the conditional probability of A when B is given and is denoted as P(A/B..

If a trial results in n-exhaustive, mutually exclusive and equally likely cases and m of them are favourable to the occurrence of an event A, then the probability of the happening of A, denoted by P(A), is given by: P(A) = m/n. Important terms are 1. Statistical or Empirical P..

Probability of an Event - So far, we have introduced the sample of an experiment and used it to describe events. In this section, we introduce probabilities associated to the events. If a trial results in n-exhaustive, mutually exclusive and equally likely cases and m of them ar..

Let us consider the random experiment of throwing a die. Let A be the event of getting an odd number on the die. \ S = {1, 2, 3, 4, 5, 6} and A = {1, 3, 5} Let B = {2, 3, 4, 5, 6}. If, after the die is thrown, we are given the information, that the event B has occurred, then the probability..