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 AND VARIABLES OF THE STATISTICAL MODEL
* 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 Verification of Genetic Information method (VGI) to the intelligence quotients of the mothers (M) and fathers (F). Function R is the mathematical expected average of the genetic ability 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 using ordinary least squares with one IQ vector of the children.
T1	IQ vector of children - Original variable from the Young Adulthood Study - Stanford-Binet intelligence test.
T4	IQ vector of children - Original variable from the Young Adulthood Study - Stanford-Binet intelligence test applied when children were 12 years old.
* WB	IQ vector of children - Original variable from the Young Adulthood Study - Wechsler Bellevueintelligence test applied when children were 13 years old.
W °	The development of artificial intelligence quotients vectors is curried out by the computer simulation 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 - Intelligence of identical and non identical twins.

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

In this graph, the three original dependent variables of the children, analyzed in the model, behave in a very different way to the progenitors' explanatory variables M & F

This graph has been selected because it can be observed that, in the q056, the similarity of the correlation line of the T1 variable with the artificial quotients of W ° intelligence is enormous, and now the same happens but with the T4 variable.

Clearly, it will not always happen and the particular graph has been chosen to call attention to this curious effect, but it has not taken more than ten minutes to find it.

In this case, it appears as if a parameters involve in the computer generation W° variable were straightened up (They were not!), or were more related to the way that functions of the human brain (relating to the different types of intelligence that the T1 and T4 test pick up) are transmitted.

Of course it also could simply be that the entire simulation model’s small randomness could produce this type of variation, which would reaffirm the virtue of the Global model due to the similar behaviour of W (vectors of artificial intelligence quotients created by its computer simulation) with original variables (directly observed IQ vectors)

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