First, we calculate the correlation coefficients between the success rate of university studies and each of these factors (**Table 3.7**).

For the current calculations, we assume that if the value of the correlation coefficient (*Corr*) is greater than or equal to 0.3, it is impractical to ignore the relationship between the indicators, and if it is greater than 0.7, we will assume that ]such a relationship is strong. When the *Corr* value is more than 0.9, the connection is superpowerful and close to functional.

Calculations confirm the lack of influence of the level of emotional intelligence on learning success. The correlation coefficient between learning success and *EI* is 0.0194. Thus, this factor does not affect the success of education not only in secondary school but also at university. The influence of the student’s level of creativity also does not affect the final learning outcome.

According to these calculations, the competition score has the greatest impact. The correlation coefficient of *CS* with *LS* is equal to 0.6886. However, the internal relationships between *IQ*,** ***ZMAT*** **and** ***i* must be taken into account.

Therefore, we apply multifactor regression analysis and build a regression model of the dependence of learning success (*LS*) on the level of emotional intelligence (*EI*),** ***IQ*, competitive score** **(*CS*), creativity** **(*CL*)** **and the number of points of the external examination in mathematics** **(*ZMAT*):

in standardized variables:

The coefficient of determination shows that 47.83 % of the fluctuation or variability of student achievement (*LS*) is due to changes in the values of the factors used in this model. This indicator can be interpreted as a statistical assessment of the quality of the model.

The rest of the *LS* variability depends on factors that are not taken into account in this model. The values of the Fisher and Darbin-Watson criteria indicate that the model is adequate and statistically qualitative.