Calculate the Z value and determine the area (probability) between the Z values. Z = (x-mu)/sigma. Z42 = (42-50)/4 = -2 & Z68 = (68-50)/4 = 4.5. Go to the related link and the areas are .0228 & for 4.5 use .9999. Subtract the two values; .9999 - .0228 = .9771.
Standard deviation doesn't have to be between 0 and 1.
Standard deviation is the square root of the variance.
From what ive gathered standard error is how relative to the population some data is, such as how relative an answer is to men or to women. The lower the standard error the more meaningful to the population the data is. Standard deviation is how different sets of data vary between each other, sort of like the mean. * * * * * Not true! Standard deviation is a property of the whole population or distribution. Standard error applies to a sample taken from the population and is an estimate for the standard deviation.
Chi-square is a distribution used to analyze the standard deviation of two samples. A t-distribution on the other hand, is used to compare the means of two samples.
The distance between the middle and the inflection point is the standard deviation.
You need to know the standard deviation or standard error to answer the question.
The mean deviation (also called the mean absolute deviation) is the mean of the absolute deviations of a set of data about the data's mean. The standard deviation sigma of a probability distribution is defined as the square root of the variance sigma^2,
It depends what you're asking. The question is extremely unclear. Accuracy of what exactly? Even in the realm of statistics an entire book could be written to address such an ambiguous question (to answer a myriad of possible questions). If you simply are asking what the relationship between the probability that something will occur given the know distribution of outcomes (such as a normal distribution), the mean of that that distribution, and the the standard deviation, then the standard deviation as a represents the spread of the curve of probability. This means that if you had a cure where 0 was the mean, and 3 was the standard deviation, the likelihood of observing a value of 12 (or -12) would be likely inaccurate if that was your prediction. However, if you had a mean of 0 and a standard deviation of 100, the likelihood of observing of a 12 (or -12) would be quite likely. This is simply because the standard deviation provides a simple representation of the horizontal spread of probability on the x-axis.
A standard normal distribution has a mean of zero and a standard deviation of 1. A normal distribution can have any real number as a mean and the standard deviation must be greater than zero.
The mean deviation for any distribution is always 0 and so conveys no information whatsoever. The standard deviation is the square root of the variance. The variance of a set of values is the sum of the probability of each value multiplied by the square of its difference from the mean for the set. A simpler way to calculate the variance is Expected value of squares - Square of Expected value.
34.1% with the normal distribution.
the t distributions take into account the variability of the sample standard deviations. I think that it is now common to use the t distribution when the population standard deviation is unknown, regardless of the sample size.
Standard deviation doesn't have to be between 0 and 1.
Standard deviation is the square root of the variance.
I think you left off some important information. Perhaps you can supply this information, to obtain assistance. To calculate the probability or the chance of occurrence between two values, we calculate: Pr{a < X < b} = F(b) - F(a) where F(x) = cumulative probability distribution. The distribution requires certain known parameters. In the case of the Normal distribution, the mean and standard deviation are parameters. In your particular case, a = 20 and b = 28.
Standard deviation is the variance from the mean of the data.
77.45%