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32 if you sample is a random sample.
Other methods look at the shape of the data and how skewed it is.
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∙ 12y ago
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Does this means that all symmetric distribution are normal Explain?
Don't know what "this" is, but all symmetric distributions are not normal. There are many distributions, discrete and continuous that are not normal. The uniform or binomial distributions are examples of discrete symmetric distibutions that are not normal. The uniform and the beta distribution with equal parameters are examples of a continuous distribution that is not normal. The uniform distribution can be discrete or continuous.
How many standard deviations is the first quartile away from the mean on a Normal distribution?
0.674 sd.
Why is the Gaussian distribution referred to as normal distribution?
Because many naturally occurring variables were approximately distributed according to a Normal bell shaped curve.Because many naturally occurring variables were approximately distributed according to a Normal bell shaped curve.Because many naturally occurring variables were approximately distributed according to a Normal bell shaped curve.Because many naturally occurring variables were approximately distributed according to a Normal bell shaped curve.
How many standard deviations is needed to capture 75 percent of data?
It depends on the shape of the distribution. For standard normal distribution, a two tailed range would be from -1.15 sd to + 1.15 sd.
How many standard normal distributions are there?
Only one. A normal, or Gaussian distribution is completely defined by its mean and variance. The standard normal has mean = 0 and variance = 1. There is no other parameter, so no other source of variability.
Why does the statistics probably fit a bell curve?
Approximately normal distributions occur in many situations, as explained by the central limit theorem. When there is reason to suspect the presence of a large number of small effects acting additively and independently, it is reasonable to assume that observations will be normal. Reference Wikipedia on Normal Distribution. See related link. Examples of bell shaped curves are t-distribution and normal distribution. There is little difference between the two curves when the sample size is greater than 30.
Why is the normal probability distribution widely used in practice?
Suppose you have a random variable, X, with any distribution. Suppose you take a sample of n independent observations, X1, X2, ... Xn and calculate their mean. Repeat this process several times. Then as the sample size increases and the number of repeats increases, the distribution of the means tends towards a normal distribution. This is due to the Central Limit Theorem. One consequence is that many common statistical measures have an approximately normal distribution.
How many outliers can a data set have?
There is no agreed definition of an outlier and consequently, there is no simple answer to the question. The number of outliers will depend on the criterion used to identify them. If you have observations from a normal distribution, you should expect around 1 in 22 observations to be more than 2 standard deviations from the mean, and about 1 in 370 more than 3 sd away. You will have more outliers if the distribution is non-normal - particularly if it is skewed.
Are all symmetric distribution are normal?
No. The binomial distribution (discrete) or uniform distribution (discrete or continuous) are symmetrical but they are not normal. There are others.
How many tails in normal distribution?
2
How many possible outcomes for normal distribution?
Infinitely many. The normal distribution is applicable to a continuous variable whose domain is the whole of the real numbers. Infinitely many. The normal distribution is applicable to a continuous variable whose domain is the whole of the real numbers. Infinitely many. The normal distribution is applicable to a continuous variable whose domain is the whole of the real numbers. Infinitely many. The normal distribution is applicable to a continuous variable whose domain is the whole of the real numbers.
Why Normal distribution is better then other distributions in statistics?
The normal distribution has two parameters, the mean and the standard deviation Once we know these parameters, we know everything we need to know about a particular normal distribution. This is a very nice feature for a distribution to have. Also, the mean, median and mode are all the same in the normal distribution. Also, the normal distribution is important in the central limit theorem. These and many other facts make the normal distribution a nice distribution to have in statistics.
What are explanation based on many observations that are supported by experimental results?
i would assume it is known as a solution
What requirements are necessary for a normal probability distribution to be a standard normal probability distribution?
The normal distribution, also known as the Gaussian distribution, has a familiar "bell curve" shape and approximates many different naturally occurring distributions over real numbers.
Why is the normal probability distribution called a family of normal probability distribution?
Because very many variables tend to have the Gaussian distribution. Furthermore, even if the underlying distribution is non-Gaussian, the distribution of the means of repeated samples will be Gaussian. As a result, the Gaussian distributions are also referred to as Normal.
Does this means that all symmetric distribution are normal Explain?
Don't know what "this" is, but all symmetric distributions are not normal. There are many distributions, discrete and continuous that are not normal. The uniform or binomial distributions are examples of discrete symmetric distibutions that are not normal. The uniform and the beta distribution with equal parameters are examples of a continuous distribution that is not normal. The uniform distribution can be discrete or continuous.
When does normal distribution occur?
The normal distribution occurs when a number of random variables, with independent distributions, are added together. No matter what the underlying probability distribution of the individual variables, their sum tends to the normal as their number increases. Many everyday measures are composed of the sums of small components and so they follow the normal distribution.
