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It means that the probability distribution function of the variable is the Gaussian or normal distribution.
The normal distribution is a theory, which works in practice (with a large enough sample). E.g if you were to plot the height of everyone in the country, you should end up with a normal distribution. Hence it is not usually considered hypothetical, in the same way that, say, imaginary numbers are hypothetical.
Assuming that you are refering to the standard normal distribution and the z-scores, the answer is 99.73%. If the assumption is incorrect, please resubmit the questionwith more information.
Writing a number in standard form simply means to express the number in its 'normal' form. Therefore, your example is written in standard form.
If the population distribution is roughly normal, the sampling distribution should also show a roughly normal distribution regardless of whether it is a large or small sample size. If a population distribution shows skew (in this case skewed right), the Central Limit Theorem states that if the sample size is large enough, the sampling distribution should show little skew and should be roughly normal. However, if the sampling distribution is too small, the sampling distribution will likely also show skew and will not be normal. Although it is difficult to say for sure "how big must a sample size be to eliminate any population skew", the 15/40 rule gives a good idea of whether a sample size is big enough. If the population is skewed and you have fewer that 15 samples, you will likely also have a skewed sampling distribution. If the population is skewed and you have more that 40 samples, your sampling distribution will likely be roughly normal.
When its probability distribution the standard normal distribution.
use this link http://www.ltcconline.net/greenl/Courses/201/probdist/zScore.htm Say you start with 1000 observations from a standard normal distribution. Then the mean is 0 and the standard deviation is 1, ignoring sample error. If you multiply every observation by Beta and add Alpha, then the new results will have a mean of Alpha and a standard deviation of Beta. Or, do the reverse. Start with a normal distribution with mean Alpha and standard deviation Beta. Subtract Alpha from all observations and divide by Beta and you wind up with the standard normal distribution.
For data sets having a normal distribution, the following properties depend on the mean and the standard deviation. This is known as the Empirical rule. About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean. So given any value and given the mean and standard deviation, one can say right away where that value is compared to 60, 95 and 99 percent of the other values. The mean of the any distribution is a measure of centrality, but in case of the normal distribution, it is equal to the mode and median of the distribtion. The standard deviation is a measure of data dispersion or variability. In the case of the normal distribution, the mean and the standard deviation are the two parameters of the distribution, therefore they completely define the distribution. See: http://en.wikipedia.org/wiki/Normal_distribution
It means that the probability distribution function of the variable is the Gaussian or normal distribution.
No, the correct phrase is "unplug." "Plug out" is not a standard term.
The normal distribution is a theory, which works in practice (with a large enough sample). E.g if you were to plot the height of everyone in the country, you should end up with a normal distribution. Hence it is not usually considered hypothetical, in the same way that, say, imaginary numbers are hypothetical.
We prefer mostly normal distribution, because most of the data around us follows normal distribution example height, weight etc. will follow normal. We can check it by plotting the graph then we can see the bell curve on the histogram. The most importantly by CLT(central limit theorem) and law of large numbers, we can say that as n is large the data follows normal distribution.
It would be more normal to say "bored with something"
Those data that were not mentioned in the original answer but which might follow a normal distribution. Since the question does not specify which ones were already listed, it is not possible to say which were "other".
Run normally
Assuming that you are refering to the standard normal distribution and the z-scores, the answer is 99.73%. If the assumption is incorrect, please resubmit the questionwith more information.
When something bad happens we say "What bad luck!"We do not say "What a bad luck!" because that is not correct in normal English usage.