STATISTICAL GRAPH

The title of each graph of the of the statistical study indicates the parents variables (R or M & F) to which the correlations are related. These correlations are represented by each point of the coloured lines corresponding to each examined C variable (children).

Likewise, the variables of unknown order, formed by the different groups of 1 to 10 values from the 70 IQ values of each parent and children variables are placed on the left hand side of the graph. The groups of 1 to 10 values located on the right hand side have been previously ordered with the variable mentioned at the bottom of the graph.

Indeed, an almost instantaneous perception of the exactitude of the particular specification of the statistical study is obtained; sixty coefficients of determination (r²) are shown in a way that highlights the global and underlying relations of the involved data set.

See the methodology of the statistical abstract for more details

DATA SOURCE VARIABLES OF THE STATISTICAL STUDY
* R °	The objective function R of the statistical study is determined in accordance with the General Theory of Conditional Evolution of Life (GTCEL), Mendelian genetics significance and applying the method VGI to the intelligence quotients of the mothers (M) and fathers (F). Function R is the mathematical expected average of the capacity of the children in agreement with the GTCEL and it will be the sum of the expected averages of each one of the cases weighed by their probabilities according the Mendelian genetics.
M & F	The two IQ vectors are use simultaneously in statistical regression with ordinary least squares and one IQ vector of the children.
T1-d	IQ vector of children - T1 with smoothed tails, 10% of X6 (Stanford Binet, Wechsler intelligence test and other)
X3	IQ vector of children - Mean of three original variables from the Young Adulthood Study (Stanford Binet, Wechsler intelligence test and other)
* X6	IQ vector of children - Mean of six original variables from the Young Adulthood Study (Stanford Binet, Wechsler intelligence test and other)
* W °	Vectors of artificial intelligence quotients are generated by the computer simulaton of the General Theory of Conditional Evolution of Life. They should behave like the observational data source.
* (M+F)/2	Vector of IQs are produced by the semi-addition of the intelligence quotients of the mother M and the father F.
* M1F1 °	Vector of IQs obtained with the lowest value of M and F of each family, either the intelligence quotient of the father or the intelligence quotient of the mother.

STATISTICAL STUDY COMMENTS

1. General statistical significance

The great increase of the correlation for the estimation of homogenous groups cannot be imputed to the reduction of 68 to 5 or 4 degrees of freedom, since the estimation with non-homogenous groups, without previous rearrangement, has the same degrees of freedom and the correlation even lowers with respect to the sample without grouping.

When the model of the statistical study has more freedom with the two intelligence quotients' variables, M and F, either it definitely adjusts better by statistical effect or the statistical data set we have available is a particular case.

In general, the model of genetic evolution of intelligence (Mendelian genetics and GTCEL) adjusts perfectly, showing an r² superior to 0.9 in several cases. Bearing in mind the tendency to increase the goodness of fit with the size of rearranged groups, we could asume it would be over 0,9 in almost all the cases for groups bigger than 20, of course, it should be needed a bigger sample...

2. Family - Sexual selection.

Family relationships are very interesting regarding genetics and intelligence, in fact, the whole IDI Study is related to family characteristics.

Some research can be done regarding relations of identical twins, non identical twins, clones and even the effect of intelligence while selecting a partner or sexual selection.

Actually, we know that all C variables correspond to mono-environmental identical twin brothers, whereas W will only be a sibling; for that reason, sometimes they will look alike and others not so much.

It does not seem hard to imagine some interesting studies on these peculiar matters.

For instance, the selection of a partner as an auxiliary mechanism of evolution has been a paradigm since the first developments of the theory of evolution. Darwin himself wrote The Descent of Man and Selection in Relation to Sex (1871) introducing a new factor, sexual selection, through which females or males choose those with the most attractive qualities as their partner.

3. Statistical significant figures of this particular graph

Although the explanation may not be very extensive, what is important here is that the model's adjustment substantially improves when this hypothesis of sexual selection is introduced. The hypothesis will affect, if introduced, only the M2 or F2 genes. These are estimated given that the measured IQs collect the power of the significant or less powerful gene, therefore, the estimations of M2 and F2 will change in light of the new information or condition introduced in the model.

The model will somewhat improve with the individual variables, but the effect will be noticed much more with the centred variables. The G-MCI with the rearrangement criterion M1F1, goes from 15.61 to 17 and the maximum r² from 0.89 to 0.97 for the objective function R (see figures Sim.cen.3 and Sexual selection).

For the objective function M&F the G-MCI is found at 17.62 while it was previously found at 17.77 and the maximum r² also rises from 0.89 to 0.97.

The maximum values of r² almost always correspond to the variable X6 or the average of six of the children's variables.