## 3 Results
### Supervised Machine Learning Models  
The second main objective of our research was to predict the GPA using other variables and factors. To meet this objective, supervised machine learning models were created. We wanted the models to achieve the highest possible accuracy for predicting the GPA. We therefore implemented several regression prediction models based on linear regression, decision tree and random forest. The partial task was to identify variables that statistically significantly affect the GPA through a causal relationship in our models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & \(\mathbf{p}\)**-Value** \\ \hline dAge & 1.489211 \(\times\) 10\({}^{-1}\) \\ dSiblings & 2.610907 \(\times\) 10\({}^{-1}\) \\ dHouseholdMembers & 3.462971 \(\times\) 10\({}^{-1}\) \\ sAverageFirst & 2.368003 \(\times\) 10\({}^{-19}\) \\ sAverageSecond & 3.293248 \(\times\) 10\({}^{-23}\) \\ sAverageHighSchool & 6.416135 \(\times\) 10\({}^{-3}\) \\ sGroupSize & 1.400622 \(\times\) 10\({}^{-2}\) \\ sSocialNetworks & 5.113872 \(\times\) 10\({}^{-3}\) \\ sCoffee & 8.288364 \(\times\) 10\({}^{-1}\) \\ sSport & 1.855124 \(\times\) 10\({}^{-1}\) \\ sSleep & 3.030258 \(\times\) 10\({}^{-2}\) \\ \hline \hline \end{tabular}  
* Acronyms as follows: dAge—age of a respondent; dSiblings—number of siblings of a respondent; dHouseholdMembers—number of household members; sAverageFirst—GPA in the first year of your university studies; sAverageSecond—GPA in the second year of your university studies; sAverageHighSchool—GPA at high school; sGroupSize—number of people you usually study with; sSocialNetworks—number of hours you spend on social networks per day; sCovffee—number of cups of coffee you drink per day; sSport—number of hours of sport during the week; sSleep—number of hours you sleep per day.  
\end{table}
Table 4: Correlation between GPA and numerical variables.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & \(\mathbf{p}\)**-Value** \\ \hline dSex & 0.6445772990 \\ dDormitory & 0.3276207182 \\ sHighSchool & 0.3817681990 \\ sHighSchoolType & 0.7749678426 \\ sRoommateLearning & 0.4693267659 \\ fPreparatoryCourses & 0.9171159973 \\ fConsultations & 0.0001257365 \\ fTypeWork & 0.3472054095 \\ fBestFocus & 0.4180869036 \\ fPracticumMath & 0.323135458 \\ fPracticumProgr & 0.2651187880 \\ fVideosAlgPS & 0.0082646657 \\ \hline \hline \end{tabular}  
* Acronyms as follows: dsex—What is your sex?; dDormitory—Do you live at dormitory or not?; sHighSchool—GPA at high school; sHighSchoolType—type of high school; sRoommateLearning—If you live at dormitory, do you study with your roommates?; PreparatoryCourses—What preparatory courses did you attend before studying at the faculty?; fConsultations—To what extent do you use consultations with the teacher?; fTypeWork—What type of seminar work suits you best?; fBestFocus—Which part of the day is your best focus?; fPracticumMath—If you have completed a course in math practice, how do you rate this course?; fPracticumProgr—If you have completed a course in programming practice, how do you rate this course?; VideosAlgPS—have you watched videos on YouTube, where the teacher from the faculty explains topics from Algebra and from Probability and Statistics? How many have you seen?  
\end{table}
Table 3: Correlation between GPA and nominal variables.