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Probability Modeling And Statistics Essay

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Project On Probability Modeling & Statistics.
Topic : Binomial Poisson and Normal.
Please mention The Measures of central tendency
The use of these distributions (in which cases these distributions are used) with illustrations.
Binomial approximation to the normal distribution.
What is Skewness and Kurtosis? How it is used and interpreted?

Binomial Distribution :
This kind of distribution is applied to single variable discrete data where results are the number of “successful outcomes” in a given scenario.
E.g. :
• no. of times the lights are red in 20 sets of traffic lights,
• No of students with green eyes in class of 40,
• No. of plants with diseased leaves from a sample of 50 plants.
Binomial distribution is used to calculate the …show more content…

Normal Distribution:
The normal distribution is a very common continuous probability distribution. Normal distribution is important in statistics and is often used in the natural and social sciences to represent real-valued random variables whose distributions are not known.
This distribution is useful because of the central limit theory. In its most general form, under some conditions which include finite variance), it states that average of random variables independently drawn from independent distributions converge in distribution to the normal, that is, become normally distributed when the number of random variables is sufficiently large.
The normal distribution is sometimes informally called the bell curve. However, many other distributors are bell-shaped( such as the Cauchy, students and logistic distribution).

Measure of Central Tendency :
The Mean, Median and Mode are all valid measures of central tendency, but under different conditions, some measures of central tendency become more appropriate to use than others.
Mean
The Mean is essentially a model of your data set. It is the value that is the most common. However, Mean is not often one of the actual values that you have observed in your data set, but it has one of its important properties is that it maximizes error in the prediction of any one value in your data set. That is, it is the value that produces the lowest amount of error

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