No it is not correct.
The variance or standard deviation.
The standard deviation stretch is used to stretch the output values using a normal distribution. The result of this stretch is similar to what is seen by the human eye.
The standard deviation and the arithmetic mean measure two different characteristics of a set of data. The standard deviation measures how spread out the data is, whereas the arithmetic mean measures where the data is centered. Because of this, there is no particular relation that must be satisfied because the standard deviation is greater than the mean.Actually, there IS a relationship between the mean and standard deviation. A high (large) standard deviation indicates a wide range of scores = a great deal of variance. Generally speaking, the greater the range of scores, the less representative the mean becomes (if we are using "mean" to indicate "normal"). For example, consider the following example:10 students are given a test that is worth 100 points. Only 1 student gets a 100, 2 students receive a zero, and the remaining 7 students get a score of 50.(Arithmetic mean) = 100 + 0(2) + 7(50) = 100 + 0 + 350 = 450/10 studentsSCORE = 45In statistics, the median refers to the value at the 50% percentile. That means that half of the scores fall below the median & the other half are above the median. Using the example above, the scores are: 0, 0, 50, 50, (50, 50), 50, 50, 50, 100. The median is the score that has the same number of occurrences above it and below it. For an odd number of scores, there is exactly one in the middle, and that would be the median. Using this example, we have an even number of scores, so the "middle 2" scores are averaged for the median value. These "middle" scores are bracketed by parenthesis in the list, and in this case are both equal to 50 (which average to 50, so the median is 50). In this case, the standard deviation of these scores is 26.9, which indicates a fairly wide "spread" of the numbers. For a "normal" distribution, most of the scores should center around the same value (in this case 50, which is also known as the "mode" - or the score that occurs most frequently) & as you move towards the extremes (very high or very low values), there should be fewer scores.
The standard deviation, in itself, cannot be high nor low. If the same measurements were recorded using a unit that was a ten times as large (centimetres instead of millimetres), the standard deviation for exactly the same data set would be 1.8. And if they were recorded in metres the sd would be 0.018
You cannot because the standard deviation is not related to the median.
When using the mean: the variance or standard deviation. When using the median: the range or inter-quartile range.
=stdev(...) will return the N-1 weighted sample standard deviation. =stdevp(...) will return the N weighted population standard deviation.
Central tendency is measured by using the mean, median and mode of a set of numbers. Variation is measured by using the range, variance and standard deviation of a set of numbers.
Standard deviation can be calculated using non-normal data, but isn't advised. You'll get abnormal results as the data isn't properly sorted, and the standard deviation will have a large window of accuracy.
No it is not correct.
The variance or standard deviation.
The standard deviation stretch is used to stretch the output values using a normal distribution. The result of this stretch is similar to what is seen by the human eye.
You calculate the standard error using the data.
The standard deviation and the arithmetic mean measure two different characteristics of a set of data. The standard deviation measures how spread out the data is, whereas the arithmetic mean measures where the data is centered. Because of this, there is no particular relation that must be satisfied because the standard deviation is greater than the mean.Actually, there IS a relationship between the mean and standard deviation. A high (large) standard deviation indicates a wide range of scores = a great deal of variance. Generally speaking, the greater the range of scores, the less representative the mean becomes (if we are using "mean" to indicate "normal"). For example, consider the following example:10 students are given a test that is worth 100 points. Only 1 student gets a 100, 2 students receive a zero, and the remaining 7 students get a score of 50.(Arithmetic mean) = 100 + 0(2) + 7(50) = 100 + 0 + 350 = 450/10 studentsSCORE = 45In statistics, the median refers to the value at the 50% percentile. That means that half of the scores fall below the median & the other half are above the median. Using the example above, the scores are: 0, 0, 50, 50, (50, 50), 50, 50, 50, 100. The median is the score that has the same number of occurrences above it and below it. For an odd number of scores, there is exactly one in the middle, and that would be the median. Using this example, we have an even number of scores, so the "middle 2" scores are averaged for the median value. These "middle" scores are bracketed by parenthesis in the list, and in this case are both equal to 50 (which average to 50, so the median is 50). In this case, the standard deviation of these scores is 26.9, which indicates a fairly wide "spread" of the numbers. For a "normal" distribution, most of the scores should center around the same value (in this case 50, which is also known as the "mode" - or the score that occurs most frequently) & as you move towards the extremes (very high or very low values), there should be fewer scores.
The standard deviation, in itself, cannot be high nor low. If the same measurements were recorded using a unit that was a ten times as large (centimetres instead of millimetres), the standard deviation for exactly the same data set would be 1.8. And if they were recorded in metres the sd would be 0.018
The answer depends on what functions are built into your calculator. Read the calculator manual.