I don't have a simple answer. I will give examples where the central limit theory seems to fail. From Wikipedia (http://en.wikipedia.org/wiki/Central_limit_theorem) From another viewpoint, the central limit theorem explains the common appearance of the 'Bell Curve' in density estimates applied to real world data. In cases like electronic noise, examination grades, and so on, we can often regard a single measured value as the weighted average of a large number of small effects. Using generalisations of the central limit theorem, we can then see that this would often (though not always) produce a final distribution that is approximately normal. Let me restate the idea of the central limit theorem: When many small, independent and random outcomes are summed, the result will eventually be normally distributed (bell shaped). The underlying processes which produce the outcome must be stationary (not changing). We state that the mean of a sample should have a normal (bell shape) distribution, if it came from a random sample. Again, the underlying population must be stationary (unchanging properties). 1) The Stock Market is an excellent example of where the central limit theory does not apply, due to the problem of non-stationary and dependent outcomes. A stock with 100 year price history does not permit me to predict the future price with a normal distribution. 2) Public opinion polls regarding politics frequently do not adhere to the central limit theory, because people are continually reacting to the media. A larger sample, taken over months, may be less reliable because people change their mind. 3) Many human traits are not the result of small random and independent factors, but of many factors interacting with each other, thus do not adhere to the bell shape curve. The quantity of alcohol we consume probably does not fit well a bell shape curve, because for a certain segment, they are addicted to alcohol.
application mean kind of theorem that we use to solve a problem, we will apply a different kind of theorem to solve one problem. it called as a application.
You use the central limit theorem when you are performing statistical calculations and are assuming the data is normally distributed. In many cases, this assumption can be made provided the sample size is large enough.
This is the Central Limit Theorem.
The central limit theorem is one of two fundamental theories of probability. It's very important because its the reason a great number of statistical procedures work. The theorem states the distribution of an average has the tendency to be normal, even when it turns out that the distribution from which the average is calculated is definitely non-normal.
yes
The Central Limit THeorem say that the sampling distribution of .. is ... It would help if you read your question before posting it.
in simplifying complex circuits and for different loads this theorem proven very useful
The Central Limit Theorem (CLT) says no such thing! In fact, it states the exact opposite.The CLT sets out the conditions under which you may use the normal distribution as an approximation to determine the probabilities of a variable X. If those conditions are not met then it is NOT OK to use the normal distribution.
The Pythagorean Theorem allows the mathematician to determine the value of the hypotenuse. The converse of the Pythagorean Theorem manipulates the formula so that the mathematician can use the values to determine that if the triangle is a right triangle.
The Central Limit Theorem (abbreviated as CLT) states that random variables that are independent of each other will have a normally distributed mean.
*SHRUGS*
The central limit theorem basically states that as the sample size gets large enough, the sampling distribution becomes more normal regardless of the population distribution.
Any two angles of a triangle determine the third angle. As a result, the side angle angle theorem is equivalent to the angle side angle theorem.
ASA
SAS
application mean kind of theorem that we use to solve a problem, we will apply a different kind of theorem to solve one problem. it called as a application.
In computational complexity theory, Cook's theorem, also known as the Cook–Levin theorem, states that the Boolean satisfiability problem is NP-complete. That is, any problem in NP can be reduced in polynomial time by a deterministic Turing machine to the problem of determining whether a Boolean formula is satisfiable.