Summary
Practical 5.2: Advanced Data Analysis: full summary + explanations
- Course
- Institution
SECOND PART of practical 5 advanced data analysis
[Show more]
1 review
By: AVL2 • 6 months ago
Not all questions were answered & not all exercises were done in this document...
Seller
Follow
Bi0med
Reviews received
Content preview
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
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller Bi0med. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $4.88. You're not tied to anything after your purchase.
Can Stuvia be trusted?
4.6 stars on Google & Trustpilot (+1000 reviews)
78252 documents were sold in the last 30 days
Founded in 2010, the go-to place to buy study notes for 14 years now