## STATISTICAL GRAPH

The title of each graph 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**

## 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 – Conditional intelligence – Gobal Cognitive Theory*) 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 EDI 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.