This document provides an introduction to the field of financial statistics. It discusses how statistics are used in everyday life and in the modern technological age to analyze large amounts of generated data. The document outlines the goals of descriptive and inferential statistics for describing and drawing inferences from data. It also discusses visual tools like charts and graphs that are used to summarize variables in datasets and provide preliminary analysis steps like cleaning data and identifying relevant questions before statistical analysis.

1. Greetings, and welcome to Financial Statistics!Greetings, and welcome to Financial Statistics!
We have all come across
Statistics in our everyday
lives. We talk about average
salaries, percentile ranks,
skew, outliers and so on. In
fact, many comparisons that
we routinely make involve
numbers. We may not be
conscious of it, but we have
been using Statistics for a
long long time.

2. Technological age
In this technological age, data
is constantly being generated
by processes such as stock
trading. Of late, the IoT or
Internet of Things movement
has begun to take shape,
driven by sensors that collect
and relay such data as one's
vital parameters, movements
on a shop floor, etc.

3. Statistics rules

4. At a high level,
statistics is a
discipline that is
dedicated to drawing
actionable insights
from available data. If
the data is indeed not
available, then it will
have to be generated
carefully via a survey,
or any other
mechanism.
Employing Statistics to Extract
Information from Data

5. There are two broad flavours of Statistics:
The goal of Descriptive
Statistics is to describe the
data, which is the subject of
study, in exact terms. On
the other hand, Inferential
Statistics focuses on
drawing inferences on the
properties of a population
by analysing the data of a
sample of the population.
Such analysis hinges on the
assumptions of
randomness and there is a
degree of uncertainty in the
inferences.

6. Most data of interest exhibit
some kind of variation: they
are rarely constant. In fact,
statistics can be considered as
the study of variation. We start
by examining a sizeable
dataset containing
international tourism receipts
for the countries of the world.
We go over preliminary steps
that we must undertake prior to
embarking on a statistical
analysis. These steps include
cleaning the data, identifying
the scales of the variables, and
drawing up a list of relevant
questions. We show how the
answers to these questions
can be derived from the data.
Descriptive
Statistics

7. Summarising Variables
Numerical measures alone are not sufficient to provide a satisfactory
description of data. Along the way, we get acquainted with visual tools
such as bar charts, pie charts, and line graphs.
Business datasets are typically made up of multiple variables, which can
be categorical or numerical. The choice of techniques we employ to
analyse the data will depend upon their scales. For instance, we use
counts to aggregate a categorical variable. A pivot table is used to
summarize data related to two categorical variables.

8. A single numerical
variable is visually
summarised with a
histogram,
whereas bivariate
numerical data is
depicted with a
scatterplot.

9. Numerical Data
Whenever there is a mix of scales within the variables of a dataset, it
makes sense to aggregate the data. For example, annual tourism
receipts (numerical) may be averaged over the various region
classifications (categorical).

10. Probability
Business processes are dynamic, and are subject to shifts
that take place within the global economic environment.
We quantify uncertain events using the framework of
probability. A multivariate census dataset forms the
backdrop of our discussions. Using this, we draw up a list
of questions concerning employees and their educational
and salary levels. We learn how to frame a problem
scenario in terms of events.

11. Spreadsheets are the life blood of business statistics. We have
used them to maintain lists of items, for account keeping. A
select few among us would have used spreadsheets for more
complex purposes, such as storing a dataset.
Make no mistake: this course relies heavily on the use of
spreadsheets. We would like you to become acquainted with a
few basic operations, well before the course gets underway.

12. Financial data
They say the Devil
is in the detail -
but business data
might be too
detailed for us to
get through all of
the individual
elements. The
tools and
techniques of
Descriptive
Statistics help us
summarise
volumes of data,
and derive
actionable
information.