Hi,
I have data for 6 different groups and for each group I have the percentage of Males and Females in that group.
This is the data:

F 
M 
1 
0.103967 
0.896489 
2 
0.070575 
0.929425 
3 
0.081081 
0.918919 
4 
0.08221 
0.91779 
5 
0.056566 
0.945455 
6 
0.083333 
0.916667 
What I would like to know is wether there is a statisticlly significant diffenrece of the percenatge of malesa and females between the groups and how could SAS find that out.
Thank you!
Assuming you have frequencies, you would do:
data have;
input g nF nM;
datalines;
1 76 655
2 65 856
3 3 34
4 61 681
5 28 467
6 1 11
;
data long;
set have;
sex = "F"; freq = nF; output;
sex = "M"; freq = nM; output;
keep g sex freq;
run;
proc freq data=long;
tables g*sex / nopercent nocol;
exact fisher / mc;
weight freq;
run;
You can look into the Chi square test  available under proc freq.
There are also other tests available under Proc Freq that may be useful. Check the documentation.
You need the frequencies (numbers of males and females) to test for differences between the proportions.
data have; input g F M; cards; 1 0.103967 0.896489 2 0.070575 0.929425 3 0.081081 0.918919 4 0.08221 0.91779 5 0.056566 0.945455 6 0.083333 0.916667 ; run; proc transpose data=have out=want name=Gender; by g; var f m; run; proc ttest data=want cochran ci=equal umpu; class Gender; var col1; run; /***************/ proc npar1way wilcoxon correct=no data=want; class Gender; var col1; exact wilcoxon; run; Two ways show the significant . there is difference between F and M . Method Variances DF t Value Pr > t Pooled Equal 10 91.57 <.0001 Satterthwaite Unequal 9.9914 91.57 <.0001 Cochran Unequal 5 91.57 <.0001 KruskalWallis Test ChiSquare 8.3077 DF 1 Pr > ChiSquare 0.0039
I don't think that's statistically valid  it's a categorical variable, ttest is for continuous variables.
@Ksharp wrote:
Since there are only two levels, the simplest way is using proc ttest(parameter method for normal data),proc npar1way (nonparameter method).data have; input g F M; cards; 1 0.103967 0.896489 2 0.070575 0.929425 3 0.081081 0.918919 4 0.08221 0.91779 5 0.056566 0.945455 6 0.083333 0.916667 ; run; proc transpose data=have out=want name=Gender; by g; var f m; run; proc ttest data=want cochran ci=equal umpu; class Gender; var col1; run; /***************/ proc npar1way wilcoxon correct=no data=want; class Gender; var col1; exact wilcoxon; run; Two ways show the significant . there is difference between F and M . Method Variances DF t Value Pr > t Pooled Equal 10 91.57 <.0001 Satterthwaite Unequal 9.9914 91.57 <.0001 Cochran Unequal 5 91.57 <.0001 KruskalWallis Test ChiSquare 8.3077 DF 1 Pr > ChiSquare 0.0039
Hi Xia,
actually in my data it is not 6 separate pairs of male and female but 6 different categories and each category has the given proportions of males and females. Unless I am mistaken (my knowledge of statistics is intermediate) the t test would test for the significance in the means of the 2 groups, therefore making a male mean of the 6 observations and a female mean of the 6 observations and comparing them. But what I would like to get is to know whether the real proportion of males and females is the same in all 6 categories, or if there are categories where the real proportions of makes and females are actually different.
Thanks!
Hi Xia,
actually I have over 4000 observations with 2 variables: 1 for the category (1 to 6) and one for gender (0 and 1). I am looking into this "Chi square test", do you think its appropriate?
Thanks!
Read up on it. You should be able to defend your decision to go with x method.
https://en.m.wikipedia.org/wiki/Chisquared_test
The chisquared test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories. Does the number of individuals or objects that fall in each category differ significantly from the number you would expect? Is this difference between the expected and observed due to sampling variation, or is it a real difference?
What Statistical should I use?
Assuming you have frequencies, you would do:
data have;
input g nF nM;
datalines;
1 76 655
2 65 856
3 3 34
4 61 681
5 28 467
6 1 11
;
data long;
set have;
sex = "F"; freq = nF; output;
sex = "M"; freq = nM; output;
keep g sex freq;
run;
proc freq data=long;
tables g*sex / nopercent nocol;
exact fisher / mc;
weight freq;
run;
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