correlation which can be strong or weak
They are some measure of the dispersion or range of numbers in the set of data.
None. Measures of central tendency are not significantly affected by the spread or dispersion of data.
You need to indicate the conditions.
distinguish between dispersion and skewness
Dispersion is the act of spreading people or things (like seeds) out over a large area. Measures of dispersion tell us the degree of variation of values in a sample or population.
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.
No
True
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 dispersion of the data.
It is a measure of the spread or dispersion of the data.
standard deviation is best measure of dispersion because all the data distributions are nearer to the normal distribution.
Central tendency is used with bidmodal distribution. This measure if dispersion is similar to the median of a set of data.?æ
They are some measure of the dispersion or range of numbers in the set of data.
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 & shape as best measures to define data.
Variance is a measure of "relative to the mean, how far away does the other data fall" - it is a measure of dispersion. A high variance would indicate that your data is very much spread out over a large area (random), whereas a low variance would indicate that all your data is very similar.Standard deviation (the square root of the variance) is a measure of "on average, how far away does the data fall from the mean". It can be interpreted in a similar way to the variance, but since it is square rooted, it is less susceptible to outliers.
None. Measures of central tendency are not significantly affected by the spread or dispersion of data.