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What are the implications for choosing different confidence levels for variability in data mining?
Perhaps the data that you are given.
What does the standard deviation of a set of data tell you?
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!
Explain what is meant when we say "data Vary". How does this variability affect the results of statistical analysis?
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Why are the measures of dispersion necessary to describe a set of data?
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.
What is the simplest measure of variability in a data set?
In a small data set, the range. However, I would not like to try and find the range for the volume of rain drops, or the size of sand grains!
