I have to characterize the behavior of demand for certain types of products, know which distribution they fit.
So, when I enter the data into the software and I do a fitting distribution test, the results show that the data follows normal distribution, but I'm confused, because how can I apply normality tests to that data, if those tests are for continuous data (Kolmogorov-Smirnov, Shapiro-Wilk, Anderson-Darling, etc)
The distribution you are testing for is continuous, not the data observations. See, for example, p. 51ff in Higgins, James J. Introduction to Modern Nonparametric Statistics, Thomson/Brooks/Cole for the description of the K-S test. Best, David
Hello Mr David, thanks for reply.
Can you please tell me what information I can find on that page, what happens is that I do not have a copy of that book. Thank you very much.
See the link below. The information is basically the same. Best wishes, David
http://itl.nist.gov/div898/handbook/eda/section3/eda35g.htm
If there is a (continuous) normal approximation to the (discrete) binomial, it shows that a discrete sample can fit into the normal distribution. First the histogram or frequency polygon should have an approximate symmetric and normal shape. By partitioning the space into intervals a chi-square goodness-of-fit test could be conducted between the observed frequencies and the corresponding expected frequencies. If the test is statistically significant, the data follow a normal distribution.
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