Integrating the Normal Distribution Curve

In summary, the conversation is discussing the normal distribution curve and the possibility of integrating it with generic parameters. It is possible to integrate it by creatively substituting variables, but only numerically, not analytically.
  • #1
Brad_Ad23
502
1
Given

[tex]P(x)= \frac{1}{\sigma \sqrt{2\pi}} e ^ \frac { -(x - \mu )^2}{2 \sigma ^2 }[/tex]

This is of course the normal distribution curve. When [tex] \mu = 0[/tex] and [tex] \sigma = 1[/tex] I can integrate this from minus infinity to positive infinity no problem using polar coordinates and a bit of multivariable calculus. The question I have, is, is it at all possible to do this if one leaves [tex] \mu[/tex] and [tex]\sigma[/tex] in as generic parameters? I would think so, but I'm not sure. No need to give a worked through example, just, is it possible at all to fit it to some form? Or is it just that with those parameters set to 0 and 1 that this is an integrable function?
 
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  • #3
Certainly. Just do a little creative substitution:
Let [tex]u= \frac{x-\mu}{\sqrt(2)\sigma^2}[/tex].

Then [tex] du= \frac{1}{\sqrt(2)\sigma^2}dx[/tex]

[tex]P(x)= \frac{1}{\sigma \sqrt{2\pi}} e ^ \frac { -(x - \mu )^2}{2 \sigma ^2 }[/tex]

becomes [tex]P(u)= \frac{1}{\sqrt{\pi}} e ^{-u^2}[/tex]

which you can integrate from negative to positive infinity as usual.
(That's why you probably have only seen that case.)
 
  • #4
Yep.
 
  • #5
can it be integrated from a number to anohter... say -2 and 2?
 
  • #6
Numerically, yes.
Analytically, no (unless you count functions like Erf(x), which is cheating in my view).
 
  • #7
Well wait a minute. Wouldn't you just change the bounds on the integral with respect to radius from 0 to infinity to 0 to 2? The theta integral would remain the same, and thus you'd get the analytical answer?
 
  • #8
Maybe I misunderstood allergic's question. I thought that he wanted to know

[tex]\int_0^2 \frac{1}{\sqrt{\pi}} e ^{-u^2} \,{\rm d}u[/tex]

I don't think there is an analytical expression for that. Numerically it's easy (0.497661).
 

FAQ: Integrating the Normal Distribution Curve

What is the normal distribution curve?

The normal distribution curve, also known as the Gaussian curve, is a probability distribution that is often used to describe real-world phenomena. It is a symmetric and bell-shaped curve that represents the probability distribution of a continuous random variable.

Why is it important to integrate the normal distribution curve?

Integrating the normal distribution curve allows us to calculate the probability of a continuous random variable falling within a certain range. This is useful in various fields such as statistics, finance, and science, where we need to make predictions and analyze data.

How do you integrate the normal distribution curve?

To integrate the normal distribution curve, you first need to standardize the variable using the z-score formula. Then, you can use a table of standard normal probabilities or a statistical software to find the probability of the variable falling within a certain range.

What is the area under the normal distribution curve?

The area under the normal distribution curve is equal to 1. This means that the total probability of all possible outcomes is 1, or 100%. This property is known as the total area rule and is a fundamental concept in probability and statistics.

What are the practical applications of integrating the normal distribution curve?

Integrating the normal distribution curve has many practical applications, such as in hypothesis testing, quality control, risk management, and predicting future events. It is also used in various statistical models, such as the normal distribution model and the central limit theorem.

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