The initial factors find their
optimum point at the fifteenth of the programmes twenty possible iterations. The
Input of application of the pretonic vowel [a] on these factors reveals that the rule
"is applied" (0.636), i.e. generally speaking, the solution [a] is maintained as
it is greater than 0.5, which figure can be attributed to the appearance of both dependent
factors.
Returning now
to this programmes logistic model formula, we can see how the calculations in the
analysis of 1L are reached:
p = Input
& Weight p0 = Input
pi = Weight pj =
Weight'
(i.e.
probab. the probab.
of where factor t
where factor o
of each
all the variable
of the 1 froup is of
the 1 group is
independent factor) rule. Here is 0.636 0.568
0.253
Returning now to this
programmes logistic model formula, we can see how the calculations in the analysis
of 1L are reached:
This equation
gives p (Input & Weight) a value that is the probability of maintenance of [a]
according to our variable rule: in the case of factor t of the 1 group, p= 0.696; if this
figure is rounded up, it corresponds to the programmes third column of results
that is, 0.70.
Thus, 1L
reveals the general input of maintenance of [a] (0.636), the weight of each factor, the
percentage of use of [a] according to each independent variable, which appears under the
name App/Total, and lastly, the probability of maintenance of [a] in relation to these
independent factors input & weight.
Once the
results are obtained, this programme displays a description of the errors between the
theoretical or expected probability and the sample used. Clearly, any probabilistic
analysis will involve error; if it did not, the field of study would be functional
mathematics. However, probability looks for the smallest margin of error between what was
expected and the real data with which it works; thus, the closer the columns that reveal
frequency (App/Total) and probability (Input & Weight), the more guarantees of success
of the analysis.
This part
contains three tests to determine whether the theoretical conditions adapt to the study
data:
-Logarithm of
likelihood (Log. likelihood)
-X-square test
-Scattergram
Using the
example of the previous analysis, we obtain:
Total
Chi-square = 150.8532
Chi-square/cell = 1.2893
Log likelihood = -1362.634
Maximum possible likelihood = -1277.040
Fit: X-square(104) = 171.187, rejected, p = 0.0000
|