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Answer:

The Normal distribution is a continuous probability distribution with possible values all the reals. Some properties of this distribution are:

Is symmetrical and bell shaped no matter the parameters used. Usually if X is a random variable normally distributed we write this like that:

[tex] X \sim N(\mu , \sigma)[/tex]

The two parameters are:

[tex]\mu[/tex] who represent the mean and is on the center of the distribution

[tex]\sigma[/tex] who represent the standard deviation  

One particular case is the normal standard distribution denoted by:

[tex]Z \sim N(0,1)[/tex]

Example: Usually this distribution is used to model almost all the practical things in the life one of the examples is when we can model the scores of a test. Usually the distribution for this variable is normally distributed and we can find quantiles and probabilities associated

Step-by-step explanation:

The Normal distribution is a continuous probability distribution with possible values all the reals. Some properties of this distribution are:

Is symmetrical and bell shaped no matter the parameters used. Usually if X is a random variable normally distributed we write this like that:

[tex] X \sim N(\mu , \sigma)[/tex]

The two parameters are:

[tex]\mu[/tex] who represent the mean and is on the center of the distribution

[tex]\sigma[/tex] who represent the standard deviation  

One particular case is the normal standard distribution denoted by:

[tex]Z \sim N(0,1)[/tex]

Example: Usually this distribution is used to model almost all the practical things in the life one of the examples is when we can model the scores of a test. Usually the distribution for this variable is normally distributed and we can find quantiles and probabilities associated

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