normal distribution in statistics

The Normal distribution: Properties of the Normal distribution Using tables of the Normal distribution The Normal distribution as a model for measurements Sampling distributions: Sampling dis..

Module One: Exploring Data - Constructing and interpreting graphical displays of distributions of univariate data: Dotplot, stemplot, histogram, cumulative frequency plot Center and spread Clusters and gaps Outliners and other unusual features Shape Summarizing distributions of ..

Conclusion - 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 and Poisson dis..

The number which tells us how many times a particular data appears is called the frequency. For example, 2 marks have been scored by five students which means marks 2 occurs five times. Therefore, the frequency of score 2 is five. Similarly, the frequency of marks 5 is three because three stu..

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..

Poisson distribution is a limiting process of binomial distribution. Poisson distribution occurs when there are events which do not occur as outcomes of a definite number of outcomes. Poisson distribution is used under the following conditions: Number of tria..

A teacher gave a test to a class of 26 students. The maximum mark is 5. The marks obtained by the pupils are: Such data as above is called ungrouped (or raw) data. We may arrange the marks in ascending or descending order. The data so represented is called an array. The difference between the great..

A trial, which has only two outcomes i.e., "a success" or "a failure", is called a Bernoulli trial. Let X be the number of successes in a Bernoulli trial, then X can take 0 or 1 and P(X =1) = p = "probability of a success" P(X = 0) = 1 - p = q = "probability of failure". Consider a random experimen..

We shall now deduce the Poisson distribution from the binomial distribution by assuming that n and p 0 such that the product np always remains finite, say l . We shall now use a very important result of limits in Calcu..

Random Variable and Probability Distribution - If 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 ..