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0:00
Welcome back, everyone.
My name is Alessio Volpicella.
I'm an associate
professor in economics
at the University
of Pavia, Italy,
and today we're
going to continue
our journey through an
introduction to econometrics
by introducing the simplest
form we may think of
in order to model relationships
among economic variables.
That is the linear
regression model.
0:29
We're going to build
upon what we saw in
the previous talk when we
discussed data in econometrics.
We introduced some key terms,
typology of data,
typologies of variables,
and argued why their
structure is going to affect
what we can conclude
about the population,
which at the end of
the day is our goal.
0:55
The roadmap for
today's video is to
introduce you to the
simplest form of model,
in order to study the
relationship among
economic variables
that is known as
a simple linear regression,
and as usual, we are
more interested in
its economic interpretation
than just the math
and stat behind it.
1:24
So say we are interested in
studying the
relationship between Y,
which is known in
mainstream textbooks
as outcome or
dependent variable,
and X, known as a predictor,
regressor, or
independent variable.
Today, for our running example,
let's say Y is the wage
of a group of workers
and X is their
years of education.
So our research question will be
what is the effect of
education on salary?
Just to have a rough idea of
what we are talking about,
let me show you a plot for
some workers in Canada.
