Hi all,
I have small sample of severity data of one disease:
data test;
infile datalines dlm="," dsd;
input severity @@;
datalines;
0,0,0,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,3,3,3,3,4,4,5,5,5,5,6,7,9,10,11
;
run;
The question is: how to reveal if the sample belongs to population with normal distribution?
How to see that 95% of values are within mu +/- 2 sigma,
68% of values are within mu +/- 1 sigma,
median is nearby mean.
Here it is the code from the adjacent topic but how to see and visualize on diagram median, mean, sigma, 2 sigma, 3 sigma.
proc univariate data=test;
var severity;
histogram severity / href=(2.0 1.0 7.0);
inset P10 median P90 / position=NE;
run;
@DmytroYermak wrote:
The question is: how to reveal if the sample belongs to population with normal distribution?
How to see that 95% of values are within mu +/- 2 sigma,
68% of values are within mu +/- 1 sigma,
median is nearby mean.
These two are not the same. Testing for normality is not the same as seeing what percent of the values are within mu ± 2 sigma, etc.
If you really want to test for normality, you might take a look at Q-Q plots in PROC CAPABILITY, and the NORMALTEST option in PROC CAPABILITY.
@DmytroYermak wrote:
for the beginners it is 'number of values within sigmas'
I object to this statement, as I don't see a need to do this computation 'number of values within sigmas' for two reasons:
But, yes, SAS can produce such inferior information if you want it.
One way.
proc stdize data=test out=temp; var severity; run; proc format library=work; value sig -1 <-<1 = '1 sigma' -2 <- -1= '2 sigma' 1 <-<2 = '2 sigma' other= 'more than 2 sigma'; run; proc freq data=temp; tables severity; format severity sig.; label severity='Standardized Severity'; run;
Proc STDIZE default standardization is STD which returns the value of a variables number of std deviations from the mean of the given variable.
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