C
CLUPE
Hi,
I am trying to make a normaldirstibutiontest.
Does anyone know how to do that in Excel.
/Chris
I am trying to make a normaldirstibutiontest.
Does anyone know how to do that in Excel.
/Chris
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J
Jerry W. Lewis
Your question is not clear. Three possible interpretations (each with
different solutions) are:
1. You want to test whether a specific data set is consistent with
having come from a normal distribution.
2. You want to evaluate the adequacy of the normal distribution random
number generator in the Analysis ToolPak.
3. You want to evaluate the numerical accuracy of NORMSDIST() and/or
NORMSINV().
Reply back with a more specific question.
Jerry
different solutions) are:
1. You want to test whether a specific data set is consistent with
having come from a normal distribution.
2. You want to evaluate the adequacy of the normal distribution random
number generator in the Analysis ToolPak.
3. You want to evaluate the numerical accuracy of NORMSDIST() and/or
NORMSINV().
Reply back with a more specific question.
Jerry
C
CLUPE
Hi,
I am looking for no. 1
/Chris
I am looking for no. 1
/Chris
Jerry W. Lewis said:Your question is not clear. Three possible interpretations (each with
different solutions) are:
1. You want to test whether a specific data set is consistent with
having come from a normal distribution.
2. You want to evaluate the adequacy of the normal distribution random
number generator in the Analysis ToolPak.
3. You want to evaluate the numerical accuracy of NORMSDIST() and/or
NORMSINV().
Reply back with a more specific question.
Jerry
J
Jerry W. Lewis
Excel has no built-in tests for that purpose.
The usual quantitative tests, such as Shapiro-Wilk would require that
you insert tabled values
http://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
or use a commercial Add-In such as
http://www.analyse-it.com/shapiro-wilk-normality-test_y.htm
You could do a rough and ready chi-square test, by dividing the real
line into a n intervals, each having an expected value under normality
of at least 5 observations with your sample size. Then sum
(Observed-Expected)^2/Expected and compare to CHIINV(alpha,n-1) where
alpha is suitably small (say 0.05)
Qualitatively, you could plot the data as a histogram and observe
whether it looks roughly bell-shaped.
A more discriminating qualitative approach would be to do a normal
probability plot. Mike Middleton has a downloadable paper on this at
http://www.usfca.edu/~middleton/data.htm
Jerry
The usual quantitative tests, such as Shapiro-Wilk would require that
you insert tabled values
http://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
or use a commercial Add-In such as
http://www.analyse-it.com/shapiro-wilk-normality-test_y.htm
You could do a rough and ready chi-square test, by dividing the real
line into a n intervals, each having an expected value under normality
of at least 5 observations with your sample size. Then sum
(Observed-Expected)^2/Expected and compare to CHIINV(alpha,n-1) where
alpha is suitably small (say 0.05)
Qualitatively, you could plot the data as a histogram and observe
whether it looks roughly bell-shaped.
A more discriminating qualitative approach would be to do a normal
probability plot. Mike Middleton has a downloadable paper on this at
http://www.usfca.edu/~middleton/data.htm
Jerry