Week 2

Question 1

Why is statistics relevant in Business?

Answer 1

Statistics plays an important role in virtually all aspects of
business (e.g. strategy, marketing, operations, supply
chain).

Question 2

What do statistics provide?

Answer 2

For example, it provides information about customers
that helps companies to create stronger marketing
campaigns and targeted advertising to increase product
sales.

Question 3

What does statistics aid in?

Answer 3

• It aids in managing financial risks, detecting fraudulent
  transactions, and preventing equipment breakdowns in
  manufacturing plants, among others.

Question 4

What are the common applications of statistics?

Answer 4

Common applications of statistics include predictive modeling,
pattern recognition, anomaly detection, classification, and
sentiment analysis.

Question 5

What are the business use cases?

Answer 5

Business use cases include, but are not limited to, the following:
* Customer analytics
* Targeted advertising
* Website personalisation
* Risk management
* Investment/ trading optimisation
* Fraud detection
* Predictive maintenance
* Logistics and supply chain management

Question 6

What are examples of statistical software?

Answer 6

Statistical software, such as Jamovi, Stata, EViews,
Minitab, and SPSS.

Question 7

What are examples of data wrangling and modelling packages?

Answer 7

Data wrangling and modelling packages/ libraries, for
instance in R (e.g. tidyr) and Python (e.g. numpy).

Question 8

What are examples of data visualisation tools?

Answer 8

Data visualisation tools and packages/ libraries, for
instance in R (e.g. ggplot2) and Python (e.g. Matplotlib).

Question 9

Why are statistical analyses inherently challenging?

Answer 9

• Statistical analyses are inherently challenging because of the
  advanced nature of the analytical process within a particular
  business context/ problem.

Question 10

What does the varied and massive amount of data add to?

Answer 10

• The varied and massive amount of data add to the complexity
  and increase the time it takes to complete projects.

Question 11

What are the challenges of large data sets?

Answer 11

• In addition, working with large datasets that may contain a
  variety of structured, unstructured and semistructured data,
  further complicates the statistical data analysis process.

Question 12

What is one of the biggest challenges?

Answer 12

One of the biggest challenges is eliminating bias in datasets
and analytical applications

Question 13

What biases can result in if they are not identified?

Answer 13

Such biases may skew results if they are not identified
and addressed, creating flawed findings that lead to
misguided decisions.

Question 14

What kind of impact can biases have?

Answer 14

Even worse, they may have a harmful impact on groups of
people.
For example, in the case of gender or racial bias in AI
systems.

Question 15

What is an additional challenging task when analysing data?

Answer 15

Thus, finding the right modelling approach and/or
appropriate data to analyse is an additional challenging
task.

Question 16

How are correct numbers and data misleading?

Answer 16

But even when numbers and data are correct, people and
organisations with their own agendas may use them to
mislead because they don’t tell the whole story/ hide
relevant facts.

Question 17

Why we must evaluate statistics critically?

Answer 17

All types of information (e.g. statistics/ data visualisation)
may be, intentional or unintentionally, misleading.
That’s why you must evaluate them critically.

Question 18

Why are data and numbers powerful tools?

Answer 18

Data and numbers are powerful tools for building
arguments by adding credibility and may help proving a
particular point.

Question 19

What can data provide?

Answer 19

Data can provide insights into the world around us and
help addressing related problems.

Question 20

What are some examples of unethical use of statistics?

Answer 20

Unethical use of statistics:
Biased sampling.
* Throwing out data that does not support
your views.
* Throwing out data without a valid
statistical reason.
* Presenting findings and analysis in
terminology designed to confuse or
overwhelm its audience.

Question 21

What is the statistical enquiry cycle?

Answer 21

Problem, Plan, Data, Analysis, Conclusion

Question 22

What does the data analysis do?

Answer 22

The data analysis process includes a set of activities that business
analysists/ data scientists perform to gather, prepare, analyse data, and
present the results/ findings to business users

Question 23

Why is data collected?

Answer 23

Data are collected for specific purposes.

Question 24

How can data be distinguished?

Answer 24

In terms of data collection, it may be distinguished
between primary and secondary.

Question 25

What does primary data refer to?

Answer 25

Primary data refers to data collected directly from the
data source without going through any existing sources
(e.g. survey conducted by a researcher, answers of an
online questionnaire).

Question 26

What does secondary data consist of?

Answer 26

▪ Secondary data consists of data previously collected
and compiled by someone else (e.g. stock market
index).

Question 27

What may data be classified as?

Answer 27

• In terms of data type, data may be classified as
  quantitative and qualitative.

Question 28

What does the statistical analysis depend on?

Answer 28

The statistical analysis that is appropriate for a particular
variable depends on whether the data for the variable are
qualitative or quantitative.

Question 29

What qualitative data can be?

Answer 29

Qualitative data are names or labels used to identify an
attribute of each element.
It may be numeric or nonnumeric (use the nominal or
ordinal scale).

Question 30

What does quantitative data represent?

Answer 30

Quantitative data represent measurements or counts.
It is always numeric (use the interval or ratio scale).
* Arithmetic operations are meaningful only with
quantitative data.

Question 31

What does level of measurement determine?

Answer 31

The level of measurement determines the amount of
information contained in the data.

Question 32

What does level of measurement also indicate?

Answer 32

The level of measurement also indicates the data
summarisation and statistical analyses that are most
appropriate.

Question 33

How many levels of measurement are there?

Answer 33

There are four levels of measurement: nominal, ordinal,
interval, and ratio.

Question 34

What does nominal data consists of?

Answer 34

Nominal data consists of labels or names used for
identification, may be non-numeric or numeric.
The categories are in no logical order and have no particular
relationship. The categories are said to be mutually exclusive
since an individual, object, or measurement can be included in
only one category.

Question 35

What is ordinal data?

Answer 35

• Ordinal data exhibits properties of nominal data and may be
  rank-ordered.
  Thus, values in one category are larger or smaller than values
  in other categories (e.g. rating: excellent, good, fair, poor).

Question 36

What is interval data?

Answer 36

Interval data have the properties of ordinal data but also
show uniform distances between successive values.

Question 37

What is ratio data?

Answer 37

Ratio data have all the properties of interval data and the
ratio of two values is meaningful.
Scale must have a natural zero point (i.e. there is a
nonarbitrary zero point).

Question 38

Why care about level of measurement?

Answer 38

• Measurement levels of most variables are not inherently fixed.
• The higher the level of measurement, the more precise the
  data is, but a precise measure does not ensure accuracy.
• You can always collapse into fewer categories, but not to more
  specific from more general.
• For example, from different education classes to degree/
  non-degree.

Question 39

Why is with every dataset critical to ask whether the contents of the dataset are understood?

Answer 39

• With every dataset, it is critical to ask whether the contents of
  the dataset are understood and well-motivated:
  – How and why were the specific cases in this dataset
  selected?
  – Is there information about the variables that we care about?
  – Why are the variables measured in the way that they are?
  – Are they good measures of the concept?

Question 40

What does big data refer to?

Answer 40

Big data refers to the large and diverse sets of information
that grow at ever-increasing rates.

Question 41

What are the three v’s of big data?

Answer 41

Three V’s of Big Data: The volume of information, velocity
(or speed) at which data are created and collected, and
the variety of data available.

Question 42

Where does big data often come from?

Answer 42

Big data often comes from data mining and arrives in
multiple formats.

Question 43

What is big data diverse in?

Answer 43

Big data is a great quantity of diverse information that
arrives in increasing volumes and with ever-higher
velocity.

Question 44

How is big data structured?

Answer 44

Big data may be structured (often numeric, easily
formatted and stored) or unstructured (more free-form,
less quantifiable).

Question 45

Where is big data often stored in?

Answer 45

Big data is most often stored in computer databases and
analysed using software specifically designed to handle
large, complex data sets.

Question 46

Where can big data be collected from?

Answer 46

Big data can be collected from publicly shared comments
on social networks and websites, voluntarily gathered
from personal electronics and apps, through
questionnaires, product purchases, and electronic check-ins, among others.