When we are dealing with data sets experimenter is interested to know two things about data set. Those are Measures of Central Tendency and Measures of Dispersion. Measures of Central Tendency are Mean, Median and Mode. Measures of Dispersion are Inter Quartile Range, Range, Mean deviation, Standard deviation and Variance. Measures of Central Tendency give the measures for center of the data. Measures of Dispersion give the measure for the variation in the data.
That is, it explains how much the data spread. Range is one type of measure of dispersion and it is the difference between maximum and minimum value in the data. It depends only on the extreme values and it neglects remaining values this is the drawback for this method. Inter Quartile Range is the difference between first and third quartiles. It gives the percentage of observations in between first and third quartile. In this case also it depends only on the first and third quartile values and neglecting remaining values. To come over from this drawback we can use Mean Deviation which includes each and every observation.
Main drawback of Mean Deviation is dealing with mathematical operations like differentiations and integrations are quite difficult. To come over from this drawback we can use Standard Deviation and Variance. Variance is the average of squared differences of the observations from their mean. Standard Deviation is the square root of the Variance. When we do not know standard Deviation for the population (It is a collection of huge similar type of items) then we have to estimate it by sample standard deviation. Symbol for Standard Deviation is’ ’. Symbol for Sample Standard Deviation is‘s’ Sample Standard Deviation Formula is .
Where ‘n’ is the sample size. Standard Deviation of the constant variable is zero. Constant variable means a variable which takes a fixed and single value. For example, a variable which takes only value 5 is called as constant variable and standard deviation of such variable is zero. For clear understanding of standard deviation, Standard Deviation Examples are discussed below. When we are Adding Standard deviations we cannot add them simply. First we have to square each individual standard deviation to make them variances. Now add these variances and take square root of this to add standard deviations. For subtracting standard deviations also we have to do the same thing. Standard Deviation Problems are not solvable directly first we have to find out the variance and then by taking square root of it we can get standard deviation. Standard Deviation Example, for the data 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Variance is 9.1667 and standard deviation is 3.02765. Mean of this data is 4.5 here the standard deviation value explains the average distance of the observations from its mean 4.5.
Know more about the online statistics help, Math Homework Help,online Math help. This article gives basic information about Standard deviation. Next article will cover more statistics concept and its advantages,problems and many more. Please share your comments.
That is, it explains how much the data spread. Range is one type of measure of dispersion and it is the difference between maximum and minimum value in the data. It depends only on the extreme values and it neglects remaining values this is the drawback for this method. Inter Quartile Range is the difference between first and third quartiles. It gives the percentage of observations in between first and third quartile. In this case also it depends only on the first and third quartile values and neglecting remaining values. To come over from this drawback we can use Mean Deviation which includes each and every observation.
Main drawback of Mean Deviation is dealing with mathematical operations like differentiations and integrations are quite difficult. To come over from this drawback we can use Standard Deviation and Variance. Variance is the average of squared differences of the observations from their mean. Standard Deviation is the square root of the Variance. When we do not know standard Deviation for the population (It is a collection of huge similar type of items) then we have to estimate it by sample standard deviation. Symbol for Standard Deviation is’ ’. Symbol for Sample Standard Deviation is‘s’ Sample Standard Deviation Formula is .
Where ‘n’ is the sample size. Standard Deviation of the constant variable is zero. Constant variable means a variable which takes a fixed and single value. For example, a variable which takes only value 5 is called as constant variable and standard deviation of such variable is zero. For clear understanding of standard deviation, Standard Deviation Examples are discussed below. When we are Adding Standard deviations we cannot add them simply. First we have to square each individual standard deviation to make them variances. Now add these variances and take square root of this to add standard deviations. For subtracting standard deviations also we have to do the same thing. Standard Deviation Problems are not solvable directly first we have to find out the variance and then by taking square root of it we can get standard deviation. Standard Deviation Example, for the data 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Variance is 9.1667 and standard deviation is 3.02765. Mean of this data is 4.5 here the standard deviation value explains the average distance of the observations from its mean 4.5.
Know more about the online statistics help, Math Homework Help,online Math help. This article gives basic information about Standard deviation. Next article will cover more statistics concept and its advantages,problems and many more. Please share your comments.
