Resume
Practical 5.2: Advanced Data Analysis: full summary + explanations
- Établissement
- Universiteit Antwerpen (UA)
SECOND PART of practical 5 advanced data analysis
[Montrer plus]
1 vérifier
Par: AVL2 • 6 mois de cela
Not all questions were answered & not all exercises were done in this document...
Aperçu du contenu
Regularized regressionThe following practical presents an exercise on multiple linear regression
models, regularization and validation.
1 Student data set
The data were obtained in a survey of students in portuguese language
courses in secondary school. It contains a lot of interesting social, gender and
study information about students.
It contains the following attributes:
1. school - student’s school (binary: ’GP’ - Gabriel Pereira or ’MS’ - Mous-
inho da Silveira)
2. sex - student’s sex (binary: "F" - female or "M" - male)
3. age - student’s age (numeric: from 15 to 22)
4. address - student’s home address type (binary: "U" - urban or "R" - rural)
5. famsize - family size (binary: "LE3" - less or equal to 3 or "GT3" -
greater than 3)
6. Pstatus - parent’s cohabitation status (binary: "T" - living together or "A"
- apart)
7. Medu - mother’s education (numeric: 0 - none, 1 - primary education
(4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher
education)
8. Fedu - father’s education (numeric: 0 - none, 1 - primary education (4th
grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher educa-
tion)
9. Mjob - mother’s job (nominal: "teacher", "health" care related, civil "ser-
vices" (e.g. administrative or police), "athome" or "other")
10. Fjob - father’s job (nominal: "teacher", "health" care related, civil "ser-
vices" (e.g. administrative or police), "athome" or "other")
11. reason - reason to choose this school (nominal: close to "home", school
"reputation", "course" preference or "other")
12. guardian - student’s guardian (nominal: "mother", "father" or "other")
13. traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to
30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
14. studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3
- 5 to 10 hours, or 4 - >10 hours)
15. failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16. schoolsup - extra educational support (binary: yes or no)
1
, 17. famsup - family educational support (binary: yes or no)
18. paid - extra paid classes within the course subject (Math or Portuguese)
(binary: yes or no)
19. activities - extra-curricular activities (binary: yes or no)
20. nursery - attended nursery school (binary: yes or no)
21. higher - wants to take higher education (binary: yes or no)
22. internet - Internet access at home (binary: yes or no)
23. romantic - with a romantic relationship (binary: yes or no)
24. famrel - quality of family relationships (numeric: from 1 - very bad to 5 -
excellent)
25. freetime - free time after school (numeric: from 1 - very low to 5 - very
high)
26. goout - going out with friends (numeric: from 1 - very low to 5 - very
high)
27. Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 -
very high)
28. Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 -
very high)
29. health - current health status (numeric: from 1 - very bad to 5 - very
good)
30. absences - number of school absences (numeric: from 0 to 93)
We can read in this data with the following command.
students<-read.csv("student-por.csv")
There are three scores included (G1, G2 for the midterms and G3 for the final
total). We will only concern ourselves with predicting the final total in this
exercise. Therefore let’s start with removing the 2 penultimate columns.
data <- students[,-
c(31,32)] summary(data)
1.1 Dummy variables
If you check the summary statistics of the data set, you will notice that there
are many categorical variables, such as gender, school, etc. We are going to
recode these into numerical features for use within our regression model. We
will do this by creating a new variable into each possibility (for example
gender-male and gender-female) and assigning a 0 or a 1. These new variables
are called ’dummy variables’. However we will only retain all but one of these
expanded variables to avoid redundancy in the dataset. For example, if we
know gender- female, then we know gender-male as well. Luckily there is an
2
Les avantages d'acheter des résumés chez Stuvia:
Qualité garantie par les avis des clients
Les clients de Stuvia ont évalués plus de 700 000 résumés. C'est comme ça que vous savez que vous achetez les meilleurs documents.
L’achat facile et rapide
Vous pouvez payer rapidement avec iDeal, carte de crédit ou Stuvia-crédit pour les résumés. Il n'y a pas d'adhésion nécessaire.
Focus sur l’essentiel
Vos camarades écrivent eux-mêmes les notes d’étude, c’est pourquoi les documents sont toujours fiables et à jour. Cela garantit que vous arrivez rapidement au coeur du matériel.
Foire aux questions
Qu'est-ce que j'obtiens en achetant ce document ?
Vous obtenez un PDF, disponible immédiatement après votre achat. Le document acheté est accessible à tout moment, n'importe où et indéfiniment via votre profil.
Garantie de remboursement : comment ça marche ?
Notre garantie de satisfaction garantit que vous trouverez toujours un document d'étude qui vous convient. Vous remplissez un formulaire et notre équipe du service client s'occupe du reste.
Auprès de qui est-ce que j'achète ce résumé ?
Stuvia est une place de marché. Alors, vous n'achetez donc pas ce document chez nous, mais auprès du vendeur Bi0med. Stuvia facilite les paiements au vendeur.
Est-ce que j'aurai un abonnement?
Non, vous n'achetez ce résumé que pour €4,49. Vous n'êtes lié à rien après votre achat.
Peut-on faire confiance à Stuvia ?
4.6 étoiles sur Google & Trustpilot (+1000 avis)
77254 résumés ont été vendus ces 30 derniers jours
Fondée en 2010, la référence pour acheter des résumés depuis déjà 14 ans