The standard deviation tells us nothing about the mean.
44.9
Standard Deviation tells you how spread out the set of scores are with respects to the mean. It measures the variability of the data. A small standard deviation implies that the data is close to the mean/average (+ or - a small range); the larger the standard deviation the more dispersed the data is from the mean.
1.41
By the definition of standard deviation, 95.46% of the normal population will be within 2 SD of the mean. Explanation: The normal distribution of a population means it follows the "bell curve". The center of this bell curve is the population's mean value. One standard deviation defines two areas (on the left and right side of the central "mean" value) under the bell curve that each have 34.13% of the population. The next standard deviation adds two additional areas under the curve, each having 13.6% of the population. Adding the areas under the curves on both sides gives us (34.13% + 13.6%) x 2 = 95.46%
The standard deviation tells us nothing about the mean.
the variation of a set of numbrs
standard deviation is the square roots of variance, a measure of spread or variability of data . it is given by (variance)^1/2
US IQ standard Deviation is 16.
44.9
Standard Deviation tells you how spread out the set of scores are with respects to the mean. It measures the variability of the data. A small standard deviation implies that the data is close to the mean/average (+ or - a small range); the larger the standard deviation the more dispersed the data is from the mean.
1.41
It gives us an idea how far away we are from the center of a normal distribution.
The purpose of obtaining the standard deviation is to measure the dispersion data has from the mean. Data sets can be widely dispersed, or narrowly dispersed. The standard deviation measures the degree of dispersion. Each standard deviation has a percentage probability that a single datum will fall within that distance from the mean. One standard deviation of a normal distribution contains 66.67% of all data in a particular data set. Therefore, any single datum in the data has a 66.67% chance of falling within one standard deviation from the mean. 95% of all data in the data set will fall within two standard deviations of the mean. So, how does this help us in the real world? Well, I will use the world of finance/investments to illustrate real world application. In finance, we use the standard deviation and variance to measure risk of a particular investment. Assume the mean is 15%. That would indicate that we expect to earn a 15% return on an investment. However, we never earn what we expect, so we use the standard deviation to measure the likelihood the expected return will fall away from that expected return (or mean). If the standard deviation is 2%, we have a 66.67% chance the return will actually be between 13% and 17%. We expect a 95% chance that the return on the investment will yield an 11% to 19% return. The larger the standard deviation, the greater the risk involved with a particular investment. That is a real world example of how we use the standard deviation to measure risk, and expected return on an investment.
It is a measure of the spread of the distribution: whether all the observations are clustered around a central measure or if they are spread out.
It allows you to understand, or comprehend the average fluctuation to the average. example: the average height for adult men in the United States is about 70", with a standard deviation of around 3". This means that most men (about 68%, assuming a normal distribution) have a height within 3" of the mean (67"- 73"), one standard deviation, and almost all men (about 95%) have a height within 6" of the mean (64"-76"), two standard deviations. In summation standard deviation allows us to see the 'average' as a whole.
The average IQ of a person in the US is around 98. IQ scores are standardized to have a mean of 100 and a standard deviation of 15.