Introduction to
STATA
Week 1 – Revision of basic statistical concepts

Teachers
•Tomáš Katrňák
•katrnak@fss.muni.cz
•Office hours: Wednesday 11.15-12.30
•Tomáš Doseděl
•dotomas@mail.muni.cz
•Office hours: upon e-mail request

Course outline
•How to deal with STATA
•Variable transformation
•Descriptive data analysis
•Multivariate data analysis

How to succeed
•Submit a short paper: till the end of semester
•Project proposal, hypotheses: till the end of 3rd week
•Descriptive analysis: till the end of 7th week
•Multivariation analysis: till the end of 10th week
•Final paper presentation
•Active participation

Assessment
•Submit a short paper: up to 23 points
•Project proposal, hypotheses: up to 10 point
•Descriptive analysis: up to 10 points
•Multivariation analysis: up to 10 points
•Final paper presentation: up to 23 points
•Active participation: up to 24 points (2 pts/week)
•
•90-100 points: A | 80-89 points: B | 70-79 points: C
•60-69 points: D | 50-59 points: E | 0-49 points: F

Revision of basic statistical concepts



Variables
•What is a variable?


Variables
•What is a variable?
•Age Gender Height Income
•„an abstraction of any possible object of the given class“

Variables
•What is a variable?
•Variable types

Variables
•What is a variable ?
•Variable types
•nominal

Variables
•What is a variable ?
•Variable types
•nominal
•name color gender occupation
•„nominate different attributes without the possibility to order them “
•
•

Variables
•What is a variable ?
•Variable types
•Nominal
•Ordinal
•
•

Variables
•What is a variable ?
•Variable types
•Nominal
•Ordinal
•Education level rank in a queue
•„we can order the values but are unable to decide their distance“
•
•

Variables
•What is a variable ?
•Variable types
•Nominal
•Ordinal
•Interval / Continuous
•
•
•

Variables
•What is a variable ?
•Variable types
•Nominal
•Ordinal
•Interval / Continuous
•Height Income Years spent in education
•„we can both order the values and decide about their distance “
•
•

Data matrix



Data matrix
Id
Gender
Age
Education
…
1
Male
19
Tertiary
2
Female
27
Secondary
3
Male
17
Primary
4
Male
23
Tertiary

Matice dat
Id
Gender
Age
Education
…
1
Male
19
Tertiary
2
Female
27
Secondary
3
Male
17
Primary
4
Male
23
Tertiary
Id
Gender
Age
Education
…
1
1
19
3
2
2
27
2
3
1
17
1
4
1
23
3

Correlation



Correlation
•The strength of a relation between two variables
•Both variables are in a relation (co-relate), if one changes, change the other
•Doesn’t necessary mean causality!

Linear regression (OLS)



Linear regression (OLS)



Linear regression (OLS)



Linear regression (OLS)
•y = a + bx
•
•a – intercept
•b – slope

Linear regression (OLS)
•Ordinary least square – tries to find a solution for which the sum of the squares is minimal.
Squares are defined by the distance of respective point from the line.