[MUSIC] Here I've got a regression table.
You will see these all the time, all the time.
It's the second most important table you'll ever see, the first being summary statistics.
These tables, second most important - still very important though - regression table.
So I just want to give you a brief overview of what it looks like, and in a different
module, I'll explain how to interpret the numbers.
So title: “OLS Regressions.”
Here I've got 1, 2, 3, 4, 5, 6, 7, 8 different regressions.
All these are called specifications: different ways of looking at the data.
So each one of these is a regression.
This is a regression, this is a regression, this is a regression, etc.
Here I've got what my dependent variable is or my outcome variable.
So, regression is all about looking at relationships between one variable and another variable.
So this is the main variable: we want to know how it's related to something else.
These variables we call the right hand side variables, or our explanatory variables.
They're the variables we want to know: how do they relate in our data to the outcome
variable?
These numbers here are called the regression coefficients.
This is what we're getting estimating from our data.
The numbers at the top are the coefficients.
Below them, these are the standard errors.
So this is a measure of this uncertainty in this point estimate, in this number.
Down here, they show us how many observations we are using.
So in this specification there's a 110, in this specification, there's only 64.
Down here, we just have some notes explaining more about the table: what's the dependent
variable, some stuffs about Columns (1) through (6), how are things measured, current prices
from the World Bank's World Development Indicator 1999, etc. etc. etc.
Some references say “see appendix table A1 ,blah blah blah.”
Because remember, when you read a paper, you should be able to read it by just looking
at the tables.
So the authors want to give you enough extra info in this table so you can understand what's
going on without actually reading any of the body text in the paper.
So another thing that's important is that we've got a bunch of different variables over
here, but there's some white space, there's some empty spots.
So that tells us what the difference between these specifications are.
So you see these first two columns have the same amount of white space.
They got a number here, numbers here, but nothing here.
The difference between this one and that one is the number of observations.
That's it.
That's the only difference.
Here we're using 110 the whole row, and here we're using just 64, which they call their
base sample.
Now these two specifications, (2) and (3) are different.
This one has a number here, whereas this one doesn't.
That means that in this specification, this variable, latitude is included in the regression,
whereas in this specification it's not, so we don't have a number here.
So now if you look at the difference between (1) and (3), we see they are using the same
number of observations but (1) doesn't have latitude and (3) does.
That's the same thing that's the difference between (4) and (3) - in (4) they're both
using the same number of observations, but this specification has these variables as
well, whereas this specifications does not.
That's why there is nothing here.
So that's the essence of what a regression table looks like, you will see them all the
time and it’s very, very important to get familiar with reading these tables.
[MUSIC].