Math and Arithmetic
Statistics
Algebra

# What are are measures of variability or dispersion within a set of data?

012 ###### 2013-03-16 20:35:41

Some measures:

Range,

Interquartile range,

Interpercentile ranges,

Mean absolute deviation,

Variance,

Standard deviation.

Some measures:

Range,

Interquartile range,

Interpercentile ranges,

Mean absolute deviation,

Variance,

Standard deviation.

Some measures:

Range,

Interquartile range,

Interpercentile ranges,

Mean absolute deviation,

Variance,

Standard deviation.

Some measures:

Range,

Interquartile range,

Interpercentile ranges,

Mean absolute deviation,

Variance,

Standard deviation.

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0 ###### 2013-03-16 20:35:41

Some measures:

Range,

Interquartile range,

Interpercentile ranges,

Mean absolute deviation,

Variance,

Standard deviation.

😂
0
🎃
0
🤨
0
😮
0

## Related Questions Sets of data have many characteristics. The central location (mean, median) is one measure. But you can have different data sets with the same mean. So a measure of dispersion is used to determine whether there is a little or a lot of variability within the set. Sometimes it is necessary to look at higher order measures like the skewness, kurtosis. Dispersion is an abstract quality of a sample of data. Dispersion is how far apart or scattered the data values appear to be. Common measures of dispersion are the data range and standard deviation. The pattern of variability - as opposed to variability itself - is termed skedasticity.  Central tendency will only give you information on the location of the data. You also need dispersion to define the spread of the data. In addition, shape should also be part of the defining criteria of data. So, you need: location, spread &amp; shape as best measures to define data. None. Measures of central tendency are not significantly affected by the spread or dispersion of data. These measures are calculated for the comparison of dispersion in two or more than two sets of observations. These measures are free of the units in which the original data is measured. If the original data is in dollar or kilometers, we do not use these units with relative measure of dispersion. These measures are a sort of ratio and are called coefficients. Each absolute measure of dispersion can be converted into its relative measure. Thus the relative measures of dispersion are:Coefficient of Range or Coefficient of Dispersion.Coefficient of Quartile Deviation or Quartile Coefficient of Dispersion.Coefficient of Mean Deviation or Mean Deviation of Dispersion.Coefficient of Standard Deviation or Standard Coefficient of Dispersion.Coefficient of Variation (a special case of Standard Coefficient of Dispersion)  The average mean absolute deviation of a data set is the average of the absolute deviations from a central point. It is a summary statistic of statistical dispersion or variability. The IQR gives the range of the middle half of the data and, in that respect, it is a measure of the variability of the data. You calculate summary statistics: measures of the central tendency and dispersion (spread). The precise statistics would depend on the nature of the data set. There is no single number. There are several different measures of central tendency - different ones are better in different circumstances. Then there are several measures of spread or dispersion, skewness and so on. All of these are characteristics of the data and they cannot all be summarised by a single number. the whole question is that The data is not perfectly linear. Identify at least 2 sources of variability in this data AND explain the effect of each? Sources of variability = outlier???? so do I just need to indicate where the outliers are??? It means that there is little variability in the data set. Generally, the standard deviation (represented by sigma, an O with a line at the top) would be used to measure variability. The standard deviation represents the average distance of data from the mean. Another measure is variance, which is the standard deviation squared. Lastly, you might use the interquartile range, which is often the range of the middle 50% of the data. Variability is an indicationof how widely spread or closely clustered the data valuesnare. Range, minimum and maximum values, and clusters in the distribution give some indication of variability.   It's a statistical tool used in psychology. A simple way of calculating the measure of dispersion is to calculate the range. The range is the difference between the smallest and largest value in a set of scores. This is a fairly crude measure of dispersion as any one high or low scale can distort the data. A more sophisticated measure of dispersion is the standard deviation which tells you how much on average scores differ from the mean.  It tells you how much variability there is in the data. A small standard deviation (SD) shows that the data are all very close to the mean whereas a large SD indicates a lot of variability around the mean. Of course, the variability, as measured by the SD, can be reduced simply by using a larger measurement scale! The data type that measures the outcome of a study is quantitative data. Floyd Buckley has written: 'Tables of dialectic dispersion data for pure liquids and dilute solutions' 'Tables of dielectric dispersion data for pure liquids and dilute solutions' -- subject(s): Dielectrics, Dispersion, Solution (Chemistry) The main need to work with grouped data was to reduce the number of data points that need to be stored and processed in calculation. With modern computers the storage and computation are not likely to be issues in many situations and so there is no need to use grouped data. 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.

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