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What is Probability Distribution?
The probability of a given event is an expression of possibility of occurrence of an event. A probability is a number which ranges from 0 to 1. Zero for an event which cannot occur and 1 for an event which is assured to occur. The joint probability of events occurring can be calculated based on the type of events. These events are with respect to each other - Independent events, mutually exclusive events, conditional probability, not mutually exclusive events, inverse probability and many more.
In other word, we say probability and statistics are so fundamentally interrelated with each other. It is difficult to discuss statistics without an understanding the meaning of probability. Probability today has become one of the basic tools of statistics.
Types of Probability Distributions
Bernoulli Distribution:It is a discrete distribution having two possible outcomes considered by n=0 and n=1 in which n=1("success") occurs with probability p and n=0("failure") occurs with probability q=p-1, where 0 < p<1 It therefore has probability density function.
Uniform Distribution:A uniform distribution is also known as a rectangular distribution. It is a probability distribution that has constant probability where variable x has a uniform distribution, denoted U(a, b) if its probability density function is f(x)=1b−a.
Binomial Distribution:It is a type of distribution that has two possible outcomes, i.e. (Success or Failure). For example, a coin toss has only two possible outcomes: heads or tails. Binomial formula b(x; n, P) = nCx * Px * (1 - P)n - x.
Normal Distribution:Normal distribution is used in the natural and social sciences to represent real-valued random variables whose distributions are not known.
Poisson Distribution:The Poisson distribution can be used to calculate the probabilities of various numbers of "successes" based on the mean number of successes.
Exponential Distribution:It also called the negative exponential distribution. It is a probability distribution that describes time between events in a poisson process. A continuous random variable X is said to have an Exponential (λ) distribution if it has probability density function fX(x|λ) = λe−λx for x > 0
Where λ > 0 is called the rate of the distribution.
Common Data Types
Discrete Data: It can take only specified values. For example, when you roll a die, the possible outcomes are 1, 2, 3, 4, 5 or 6 and not 1.5 or 2.45.
Continuous Data: It can take any value within a given range. The range may be finite or infinite. For example- weight of a person. The weight can be any value from 5o kgs, or 52.5 kgs, or 52.5436kgs.
What Are The Topics Covered By Our Experts?
We also covered probability related topics given below. You can seek probability homework help or probability assignment help on any concept of probability from our professional experts. They are the great at writing probability assignments.
Inequalities and convergence
Distributions of random variables
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