Statistics for business decision making and analysis robert stine and foster chapter 02

Chapter 2

Data

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2.1 Data Tables
Some Basic Ideas


Data are a collection of numbers, labels, or
symbols with context



A data table is a rectangular arrangement of data
with rows and columns



Observations or cases form the rows; common
attributes or variables form the columns
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2.1 Data Tables
Disorganized Data

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2.1 Data Tables
Same Data in a Data Table

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2.1 Data Tables


Organize data to yield meaningful information



Provide context (e.g., who, what, when)



Improve interpretability with meaningful names,
formatting and units

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2.2 Categorical and Numerical Data

Categorical Data


Also called qualitative or nominal variables



Identify group membership



Type of purchase made and Brand of bike are
examples

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2.2 Categorical and Numerical Data
Numerical Data


Also called quantitative or continuous variables



Describe numerical properties of cases



Have measurement units



Size of bike (cm) and Amount spent ($) are
examples
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2.2 Categorical and Numerical Data
Measurement Scales





Nominal – name categories without implying
order (categorical)
Ordinal – name categories that can be ordered
(categorical)
Interval – numerical values that can be added or
subtracted (no absolute zero)
Ratio – numerical values that can be added,
subtracted, multiplied or divided (makes ratio
comparisons possible)
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2.2 Categorical and Numerical Data
Likert Scale (Ordinal – 5 to 7 Categories)

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2.3 Recoding and Aggregation


Recode: building a new variable from another
(recoding price into expensive or inexpensive)



Aggregate: reduce rows in a data table by
counting or summing values within categories

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2.3 Recoding and Aggregation
An Example of Aggregation

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4M Example 2.1: MEDICAL ADVICE

Motivation
Are patients from one HMO more likely to visit the
doctor than those from another HMO?

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4M Example 2.1: MEDICAL ADVICE
Method
Gather data and organize in a data table. Cases
that make up the rows are office visits. The
following variables make up three columns:
Patient ID; HMO Plan; and Duration of patient’s
office visit.

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4M Example 2.1: MEDICAL ADVICE
Mechanics

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4M Example 2.1: MEDICAL ADVICE
Message
Aggregate the duration of office visits to learn

whether patients from one plan are consuming
most of the doctor’s office time.

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2.4 Time Series
Some Definitions


Time series – data recorded over time



Timeplot – graph of a time series showing values
in chronological order



Frequency – regular time spacing of data in a
time series (e.g., daily, monthly, etc.)



Cross-sectional – data observed at the same time
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2.4 Time Series
Timeplot of Monthly Unemployment Rate

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2.5 Further Attributes of Data
Useful to Know


When and where the data were collected



Source of the data (available online?)



How the data were collected

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4M Example 2.2: CUSTOMER FOCUS
Motivation
How do customers in a focus group react to a
new product design?

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4M Example 2.2: CUSTOMER FOCUS
Method
Gather data and organize in a data table. The
cases that make up the rows are participants in
the focus group. One of the variables that make
up the columns is participants’ ratings of the
product.

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4M Example 2.2: CUSTOMER FOCUS
Mechanics
In addition to product ratings, the columns should
include characteristics of the participants such as
name, age (in years), sex, and income.

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4M Example 2.2: CUSTOMER FOCUS
Message
Determine who likes the design (younger or more
affluent members of the focus group, for example)

and choose advertising that appeals to this group.

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Best Practices


Provide a context for your data.



Use clear names for your variables.



Distinguish numerical data from categorical data.



Track down the details when you get the data.



Keep track of the source of data.
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Pitfalls


Do not assume that a list of numbers provides
numerical data.



Don’t trust all of the data that you get from the
Internet.



Don’t believe every claim based on survey data.

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