Chebyshevs Theorem and The Empirical Rule

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Chebyshev’s Theorem and The Empirical Rule

Suppose we ask 1000 people what their age is. If this is a representative sample then there will be very few people of 1-2 years old just as there will not be many 95 year olds. Most will have an age somewhere in their 30’s or 40’s. A list of the number of people of a certain age may look like this:

Next, we can turn this list into a scatter diagram with age on the horizontal axis and the number of people of a certain age on the vertical axis.

From the statistical point of view a scatter diagram may have two shapes.  

It may be shaped or at least looks approximately like a 'bell curve', which looks like this:

        

A 'bell curve' is perfectly symmetrical with respect to a vertical line through its peak and is sometimes called a "Gauss curve" or a "normal curve".

The second shape a scatter diagram may have is anything but a normal curve as in the next drawing:

We can do a lot of good statistics with the normal curve, but virtually none with any other curve.

Let us assume that we have recorded the 1000 ages and computed the mean and standard deviation of these ages. Assuming the mean age came out as 40 years and the standard deviation as 6 years we can do the following predictions.

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Chebyshev’s Theorem

In the case of a scatter diagram that seems to be anything but a normal curve, all we can go by is Chebyshev’s theorem. This very important but rarely used theorem states that in those cases where we have a non-normal distribution, the following can be said abut the individual data, which in this case are the ages:

  • At least 75% of all the ages will lie in the range of .
    In our case this means that at least 75% of the people will have an age in ...

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