# Data Variables

## Data Variables

Dr. Asish Satpathy

Data

## Qualitative

Is descriptive and conceptual and cannot be measured

## Quantitative

Can be counted, measured, and expressed using numbers

### Example: Starbucks Franchise

Starbucks-3 N Dobson Rd

City State # of Employees Sales Volume

Starbucks-1 E Jefferson St Phoenix Arizona

Starbucks-2 College of Business Tempe Arizona

Chandler Arizona

7

15

10

\$345,000

\$739,000

\$493,000

Starbucks-4 E Warner Rd Gilbert Arizona

Starbucks-5 W Southern Ave Mesa Arizona

Starbucks-6 N Hayden Rd ScottsdaleArizona

Starbucks-7 N Scottsdale Rd ScottsdaleArizona

6

14

24

12

\$296,000

\$690,000

\$1,182,000

\$591,000

### Categorical: Nominal

 In nominal measurement the numerical values just

"name" the attribute uniquely.

 A player with number 24 is not more of anything than a player with number 23, and is certainly not better than number 23.

 Numbers are used to classify (male or female) or categorize (Color) – can be stored as “word”, “text” or

“nominal code”.

Example: Employment Classification

 1 for Educator

 2 for Construction Worker

 3 for Manufacturing Worker

### Quiz-1

 Can you find mean or average value of nominal data? Yes or No?

Characterized as:

(1)Frequency

(2)Percentage

### Categorical: Ordinal

 Categorical data can be on an ordinal scale. Numbers are used to indicate rank or order

 Relative magnitude of numbers is meaningful

 Differences between numbers are not comparable

Example: Difference between strongly agree and agree is not necessarily same as the difference between disagree and strongly disagree.

Strongly

Agree

1

Agree

2

Neutral

3

Another example (rank value as shown below)

 1 for President

 2 for Vice President

 3 for Plant Manager

Disagree

4

Strongly

Disagree

5

### Quiz-2

 Can you find mean or average value of ordinal data?

Characterized as:

(1)Frequency

(2)Percentage

### Numerical: Interval

 Also known as “Scale”, “Quantitative” or “Parametric” data

 Distances between consecutive integers are equal

 Relative magnitude of numbers is meaningful

 Differences between numbers are comparable

 Location of origin, “zero”, is arbitrary

 Data are always numerical

 Example: Temperature at different rooms in a home

### Numerical: Ratio

 Ratio is very similar to the interval scale, with the difference that it has a true zero point.

 This scale is commonly used for values that are measured in numbers, such as length, height, weight, or monetary values like cost and revenue.

 Relative magnitude of numbers is meaningful

 Differences between numbers are comparable

 Location of origin, zero, is absolute (natural)

Examples: Height, Weight, and Volume;

Monetary Variables, such as Profit and Loss, Revenues;