Microsoft Office Tutorials and References
In Depth Information
Using NORM. DIST and POISSON. DIST to Determine Probabilities
Figure 11-7 shows an example of using the GROWTH function to forecast
exponential data. Columns A and B contain the known data, and the range
D10:D19 contains the X values for which predictions are desired. The
GROWTH array formula was entered in E10:E19. The chart shows a
scatter plot of the actual data, up to X = 40, and the projected data, for X values
above 40. You can see how the projected data continues the exponential
curves that are fit by the actual data.
Figure 11-7:
Demonstrating
use of the
GROWTH
function
to project
exponential
data.
Using NORM.DIST and POISSON.DIST
to Determine Probabilities
You can get a good introduction to the normal distribution in Chapter 9. To
recap briefly, a normal distribution is characterized by its mean (the value
in the middle of the distribution) and by its standard deviation (the extent to
which values spread out on either side of the mean). The normal
distribution is a continuous distribution, which means that X values can be fractional
and aren’t restricted to integers. The normal distribution has a lot of uses
because so many processes, both natural and human, follow it.
NORM.DIST
The word normal in this context doesn’t mean “good” or “okay,” and a
distribution that is not normal is not flawed in some way. Normal is used simply to
mean “typical” or “common.”
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