Hello, my name is
Dr. Brian Blank
and I'm a finance faculty member
at Mississippi State University.
Today I will be discussing
the fundamentals of data analysis.
Specifically, we will focus on
how to use large datasets
once the data have been
gathered and cleaned.
As of late, one of the topics
frequently covered by the media
and researches alike is the use of big data,
because data are becoming
being able to analyze large datasets
is more important than ever.
Datasets are simply
a collection of observations
or individual bits of information
coded together in a uniform manner.
But using a large dataset
will allow us to learn more about reality.
Thousands of observations
can be used to understand
what is taking place
in the outside world.
This is simply due to large quantities
of computational power
that are now available
with modern technology.
As data become more common
and larger than before,
datasets will increasingly be used
and data analysis will be more informative
than it has been before.
With more information,
we can learn using more data
and this will add additional value
to professionals in various industries.
Before we begin, I want to talk
about some of the ideas
that we will be discussing today.
We'll first discuss ways
to summarize our data.
which are also sometime referred to
as descriptive statistics,
can be used to help us understand the data
that we'll be analyzing.
The statistics describe
characteristics of the data,
as well as providing an idea
what observations are
or are not included in our dataset.
Next, we'll spend time analyzing data
and summarizing the relation
In particular, univariate analysis
is simply analysis using a single variable
and how it relates to another,
as a result, we'll begin with reviewing
how two variables are correlated,
which tells us how changes in one relate
to changes in another variable.
Then we will incorporate additional variables
and perform multivariate analysis.
On the other hand,
multivariate analysis allows us
to incorporate information
that could be related
to our variable of interest
and provides additional confidence
in our results by controlling
for the effects of these other variables.
For the purposes of our discussion today,
we will focus on financial information,
that is information about a firm
that comes from financial statements.
However, most of the principles
are also easily applicable
to other forms of data.
An example of multivariate analysis
using financial information
could be a situation
where we want to know
if it is more profitable for a firm
to hold higher levels of debt
in order to finance to firm's assets.
However, we may want to consider
the size of the firm,
even if it is not our main variable
Size is important
because large and small firms
may have other differences between them,
they could also influence
the firm's profitability and debt holdings.
We'll discuss these ideas more
later on in our analysis.