Normal distribution and extremal value

In summary, the conversation is about calculating the value of sigma for the normal distribution in order to find the maximal probability for a given interval. The Excel calculation gives a value of 1.471, but an analytical solution is also sought. The problem is that the function exp(-x²) is unintegrable, so a table for the normal distribution probability is used instead. The key to finding an analytical solution is calculating the derivative of the integral of the distribution function over the given interval.
  • #1
faruk
5
0
Normal distribution.

What is the value of sigma (dispersion) for maximal probability P(1<x<2) ?

Excel calculation: sigma is about 1.471. But what would be an analytical solution?

http://img500.imageshack.us/img500/558/normdistrib19ql.gif
 
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  • #2
HINT: Calculate the derivative with respect to [itex]\sigma[/itex] of the integral of the distribution function over the given interval.
 
  • #3
Tide said:
HINT: Calculate the derivative with respect to sigma of the integral of the distribution function over the given interval.

That's exact the problem.
exp(-x²) belongs to the unintegratable functions. It's the cause we use the table of the normal distribution probability.

I hope to be wrong. Please help me.
 
  • #4
faruk said:
That's exact the problem.
exp(-x²) belongs to the unintegratable functions. It's the cause we use the table of the normal distribution probability.

I hope to be wrong. Please help me.

Your precise wording is wrong. exp(-x2) is integrable- it's integral just doesn't happen to be an elementary function. (Actually, its integral is the error function because that's how the error function is defined!)

But you don't need to know the function itself you only need to know its derivative. What is this derivative:
[tex]\frac{d}{dx}\left(\int_a^x e^{-t^2}dt\right)[/tex]

Hint: What is this derivative:
[tex]\frac{d}{dx}\left(\int_a^x f(t)dt\right)[/tex]
 

FAQ: Normal distribution and extremal value

What is normal distribution?

Normal distribution, also known as Gaussian distribution, is a probability distribution that is symmetric and bell-shaped. It is commonly used in statistics to model continuous random variables such as height, weight, and IQ scores.

What are the characteristics of normal distribution?

The characteristics of normal distribution include a symmetric bell-shaped curve, with the mean, median, and mode all equal, and the empirical rule stating that approximately 68% of the observations fall within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations.

How is normal distribution related to extremal value?

Extremal value theory is used to model the extreme values of a sample drawn from a population, assuming that the distribution of these extreme values follows a generalized extreme value (GEV) distribution. The GEV distribution is a generalization of the normal distribution and can be used to model both the upper and lower tails of a distribution.

What is the use of normal distribution in real-life situations?

Normal distribution is used in many real-life situations, such as in quality control to determine if a product meets certain standards, in finance to model stock prices, and in psychology to study human behavior and intelligence. It is also commonly used in hypothesis testing and confidence intervals.

How is normal distribution different from other probability distributions?

Normal distribution is unique in that it is the only continuous probability distribution that is symmetric and bell-shaped. It is also the most commonly used distribution due to the central limit theorem, which states that the sum of a large number of independent random variables will be approximately normally distributed, regardless of the underlying distribution of the individual variables.

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