Q: Is the normal distribution infinite

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The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.

It means distribution is flater then [than] a normal distribution and if kurtosis is positive[,] then it means that distribution is sharper then [than] a normal distribution. Normal (bell shape) distribution has zero kurtosis.

No. Normal distribution is a special case of distribution.

The normal distribution can have any real number as mean and any positive number as variance. The mean of the standard normal distribution is 0 and its variance is 1.

No. The binomial distribution (discrete) or uniform distribution (discrete or continuous) are symmetrical but they are not normal. There are others.

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Because the domain of the normal distribution is infinite - in both directions.

1

The domain is infinite but the range is finite.

The total area under a normal distribution is not infinite. The total area under a normal distribution is a continuous value between any 2 given values. The function of a normal distribution is actually defined such that it must have a fixed value. For the "standard normal distribution" where μ=0 and σ=1, the area under the curve is equal to 1.

No. The curve in a normal distribution goes on out to plus and minus infinity. You might never see any observations out there, but if you were to make an infinite number of observations, you theoretically would.

The standard normal distribution is a normal distribution with mean 0 and variance 1.

The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.

le standard normal distribution is a normal distribution who has mean 0 and variance 1

When its probability distribution the standard normal distribution.

No, the normal distribution is strictly unimodal.

Yes. When we refer to the normal distribution, we are referring to a probability distribution. When we specify the equation of a continuous distribution, such as the normal distribution, we refer to the equation as a probability density function.

The Normal distribution is, by definition, symmetric. There is no other kind of Normal distribution, so the adjective is not used.