1.2 Organizing, Visualing, and Describing Data

Question 1

Numerical data (a.k.a. quantitative data)

Answer 1

Values that represent measured/counted quantities

Question 2

two types of numerical data

Answer 2

continuous data and discrete data

Question 3

continuous data

Answer 3

Data may take on any numerical value in a specified range of values

ex: Any value between 0 and 1 (Infinite number of possibilities)

Question 4

discrete data

Answer 4

Data may only take on a countable number of values

ex: 0, 0.5, and 1 (Only 3 possible values)

Question 5

Categorical data (a.k.a. qualitative data)

Answer 5

Values that describe the characteristic of a group of observations

For example, companies can be classified into bankrupt vs. not bankrupt

Question 6

two types of categorical data

Answer 6

nominal data and ordinal data

Question 7

what is a variable

Answer 7

Characteristic/quantity that can be measured and is subject to change (e.g., stock price)

Question 8

what is an Observation

Answer 8

A value of the variable that is collected (e.g., stock price yesterday was $30)

Question 9

Cross-sectional data

Answer 9

observations that capture characteristics of different units at a specific point in time

An example of this is a list that shows the current dividend yields of different FTSE 100 companies.

Question 10

Time-series data

Answer 10

observations of the same unit at different points in time

An example of this is a list that shows the dividend yield of an FTSE 100 company over the past 10 years

Question 11

Panel data

Answer 11

a mix of time-series and cross-sectional data

Question 12

Structured data

Answer 12

highly organized in a pre-defined manner with repeating patterns

They are relatively easy to store, search, and analyze

Common examples of structured data include market data and fundamental data stored in Excel databases

Question 13

unstructured data

Answer 13

do not follow any conventionally organized forms

They typically require manual processing prior to being analyzed by financial models

Common examples of unstructured data include text (from financial news), audio, video, and photo

helped byalternative data

Question 14

alternative data

Answer 14

the data generated through unconventional sources (e.g., individual social media posts, satellite imagery, etc.), drives the availability and importance of unstructured data

Question 15

Raw data

Answer 15

data available in the original form as collected

They normally cannot be used directly to extract information

Question 16

the first step to be able to use raw data

Answer 16

usually to organize them into a one-dimensional array or two-dimensional array

Question 17

one-dimensional array

Answer 17

suitable for a single variable

For example, a one-dimensional array can be built to show the annual return of the S&P 500 index for the past 10 years.

–> This is appropriate because the annual return is the only variable that needs to be evaluated

Question 18

A two-dimensional rectangular array (or data table)

Answer 18

used to analyze multiple variables

For example, in addition to the annual return, a data table can also show the dividend yield and earnings yield of the S&P 500 index for the 10-year period

Question 19

An analyst uses a software program to analyze unstructured data—specifically, management’s earnings call transcript for one of the companies in her research coverage. The program scans the words in each sentence of the transcript and then classifies the sentences as having negative, neutral, or positive sentiment. The resulting set of sentiment data would most likely be characterized as:

A. ordinal data.

B. discrete data.

C. nominal data.

Answer 19

A. ordinal data.