The normal distribution is one of the cornerstones of probability theory and statistics, because of the role it plays in the Central Limit Theorem, because of its analytical tractability and because many real-world phenomena involve random quantities that are approximately normal (e.g., errors in scientific measurement).
It is often called Gaussian distribution, in honor of Carl Friedrich Gauss (1777-1855), an eminent German mathematician who gave important contributions towards a better understanding of the normal distribution.
Sometimes it is also referred to as "bell-shaped distribution", because the graph of its probability density function resembles the shape of a bell.
As you can see from the above plot of the density of a normal distribution, the density is symmetric around the mean (indicated by the vertical line). As a consequence, deviations from the mean having the same magnitude, but different signs, have the same probability. The density is also very concentrated around the mean and becomes very small by moving from the center to the left or to the right of the distribution (the so called "tails" of the distribution). This means that the further a value is from the center of the distribution, the less probable it is to observe that value.
The remainder of this lecture gives a formal presentation of the main characteristics of the normal distribution, dealing first with the special case in which the distribution has zero mean and unit variance, then with the general case, in which mean and variance can take any value.
Table of contents
The adjective "standard" indicates the special case in which the mean is equal to zero and the variance is equal to one.
Standard normal random variables are characterized as follows.
Definition Let be an absolutely continuous random variable. Let its support be the whole set of real numbers:We say that has a standard normal distribution if its probability density function is
The following is a proof that is indeed a legitimate probability density function:
The function is a legitimate probability density function if it is non-negative and if its integral over the support equals 1. The former property is obvious, while the latter can be proved as follows:
The expected value of a standard normal random variable is
It can be derived as follows:
The variance of a standard normal random variable is
It can be proved with the usual variance formula ():
The moment generating function of a standard normal random variable is defined for any :
It is derived by using the definition of moment generating function:The integral above is well-defined and finite for any . Thus, the moment generating function of exists for any .
The characteristic function of a standard normal random variable is
By the definition of characteristic function, we haveNow, take the derivative with respect to of the characteristic function:Putting together the previous two results, we obtainThe only function that satisfies this ordinary differential equation (subject to the condition ) is
There is no simple formula for the distribution function of a standard normal random variable because the integralcannot be expressed in terms of elementary functions. Therefore, it is usually necessary to resort to special tables or computer algorithms to compute the values of . The lecture entitled Normal distribution values discusses these alternatives in detail.
While in the previous section we restricted our attention to the special case of zero mean and unit variance, we now deal with the general case.
The normal distribution with mean and variance is characterized as follows.
Definition Let be an absolutely continuous random variable. Let its support be the whole set of real numbers:Let and . We say that has a normal distribution with mean and variance , if its probability density function is
We often indicate the fact that has a normal distribution with mean and variance by
To better understand how the shape of the distribution depends on its parameters, you can have a look at the density plots at the bottom of this page.
The following proposition provides the link between the standard and the general case.
Proposition If has a normal distribution with mean and variance , thenwhere is a random variable having a standard normal distribution.
This can be easily proved using the formula for the density of a function of an absolutely continuous variable ( is a strictly increasing function of , since is strictly positive):
Thus, a normal distribution is standard when and .
The expected value of a normal random variable is
The proof is a straightforward application of the fact that can we written as a linear function of a standard normal variable:
The variance of a normal random variable is
It can be derived as follows:
The moment generating function of a normal random variable is defined for any :
The mgf is derived as follows:It is defined for any because the moment generating function of is defined for any .
The characteristic function of a normal random variable is
The derivation is similar to the derivation of the moment generating function:
The distribution function of a normal random variable can be written aswhere is the distribution function of a standard normal random variable (see above). The lecture entitled Normal distribution values provides a proof of this formula and discusses it in detail.
This section shows the plots of the densities of some normal random variables. These plots help us to understand how the shape of the distribution changes by changing its parameters.
The following plot contains the graphs of two normal probability density functions:
the first graph (red line) is the probability density function of a normal random variable with mean and standard deviation ;
the second graph (blue line) is the probability density function of a normal random variable with mean and standard deviation .
By changing the mean from to , the shape of the graph does not change, but the graph is translated to the right (its location changes).
The following plot shows two graphs:
the first graph (red line) is the probability density function of a normal random variable with mean and standard deviation ;
the second graph (blue line) is the probability density function of a normal random variable with mean and standard deviation .
By increasing the standard deviation from to , the location of the graph does not change (it remains centered at ), but the shape of the graph changes (there is less density in the center and more density in the tails).
The following lectures contain more material about the normal distribution.
How to tackle the numerical computation of the distribution function
Multivariate normal distribution
A multivariate generalization of the normal distribution, frequently encountered in statistics
Quadratic forms involving normal variables
Discusses the distribution of quadratic forms involving normal random variables
Linear combinations of normal variables
Discusses the important fact that normality is preserved by linear combinations
Below you can find some exercises with explained solutions.
Let be a normal random variable with mean and variance . Compute the following probability:
First of all, we need to express the above probability in terms of the distribution function of :
Then, we need to express the distribution function of in terms of the distribution function of a standard normal random variable :
Therefore, the above probability can be expressed aswhere we have used the fact that , which has been presented in the lecture entitled Normal distribution values.
Let be a random variable having a normal distribution with mean and variance . Compute the following probability:
We need to use the same technique used in the previous exercise (express the probability in terms of the distribution function of a standard normal random variable):where we have found the value in a normal distribution table.
Suppose the random variable has a normal distribution with mean and variance . Define the random variable as follows:Compute the expected value of .
Remember that the moment generating function of isTherefore, using the linearity of the expected value, we obtain
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