For details of the advanced properties available for column significance testing, see advanced.
Column/row significance testing allows you to compare the column profile for each column/row in pairs.
In the results, significant values will be indicated by letters, as follows (if the corresponding display properties are selected):
High significance: "A+"
Medium significance: "A"
Low significance: "a"
p1= Proportion 1 observed in the sample
n1=Sample size
p2= Proportion 2 observed in the sample
n2=Sample size
tα= 90% = 1.65
tα= 95% = 1.96
tα= 99% = 2.576
We calculate 'D', which follows a normal mathematical expectation law p1-p2, and standard deviation sd=squared ROOT((p1*(1-p1))/n1 + (p2*(1-p2))/n2),
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(P1-P2) D= -------------------------------------------------------- P1*(1-P1) P2 * (1 - P2) Root (----------------- + ------------------- ) N1 N2 |
=> if abs(D)> tα = Significant difference
m1= Mean 1 observed in the sample
sd1= Standard deviation 1 observed in the sample
n1=Sample size
m2= mean 2 observed in the sample
sd2= Standard deviation 2 observed in the sample
n2=Sample size
tα= 90% = 1.65
tα= 95% = 1.96
tα= 99% = 2.576
We calculate 'D', which follows a normal mathematical expectation law m1-m2=0, and standard deviation sd=root( sd1 ²/ n1 + sd2²/n2),
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(m1-m2) D= -------------------------------------------------------- squared root( sd1 ²/ n1 + sd2²/n2) |
=> if abs(D)> tα = Significant difference