normal probability distribution

It is often very important to allocate a numerical value to an outcome of a random experiment. For example, consider an experiment of tossing a coin twice and note the number of heads (x) obtained. Outcome HH HT TH TT No. of heads (x) 2 1 1 0 x is called a random variable, which can assume the valu..

Cumulative Frequency Distribution - Cumulative frequency is obtained by adding the frequency of a class interval and the frequencies of the preceding intervals upto that class interval. This is explained by an example below. The following frequency distribution table gives the m..

We have P(X = x + 1) = n C x + 1 p x + 1 q n - x - 1 ..

Let X be continuous random variable which can assume values in the interval [a,b]. A function f(x) on [a,b] is called the probability density function if ..

Introduction - Suppose the two events are not independent, that is the occurrence of one depends on the occurrence of other, then how do we compute This can be explained by conditional probability. Baye's theorem is named after the British mathematician Thomas Bayes who published it in a ..

n and p are called the parameters of the binomial distribution..

In this chapter we have studied the method of evaluating probabilities of events relating to independent events and conditional events. We have also studied about random variables and their probability distributions, namely binomial distribution a..

Although the probability distribution of a continuous random variable cannot be presented in tabular form, it can have a formula in the form of a function represented by f(x) usually called the probability density functio..

A random experiment which has only two outcomes (success or failure) Let n independent bernoulli trials be performed and X denote the number of successes in n trials then X follows a binomial distribution. The probability of success be p, The probability of fail..

Mean deviation is easy to calculate. This measure is simple to understand. This can be calculated from any average. Its value is based upon all the (observations) items of the data. It is less affected by extreme observations. It gives us an idea about how the items are situated in the..