# Data Types

In the context of statistics and data analysis, data types refer to the nature or characteristics of the values that a variable can take. There are two main types of data:&#x20;

1. **Categorical**: Groups (e.g. brands, gender, yes/no)
2. **Numeric**: Numbers (Discrete (finite) and Continuous(infinite))
   1. **Discrete Data:** Discrete data consists of whole, distinct values, typically counting numbers. Examples include:

      * Number of customers in a coffee shop
      * Number of students in a class
      * Number of cars in a parking lot

   2. **Continuous Data:** Continuous data is characterized by an infinite number of possible values within a given range and often involves measurements. Unlike discrete data, fractional values are meaningful and can represent any point along a continuum. Examples include:
      * Height of individuals
      * Weight of an object
      * Temperature in degrees Celsius

There are also two ways to categorize the data:

* Based on Types of Data
  * Categorical
  * Numerical
* Based on Level of Measurement
  * Qualitative (Nominal and Ordinal) -> Related to Categorical Data
  * Quantitative (Interval (No True Zero) and Ratio (True Zero)) -> Related to Numeric Data

Let's explore data types based on Level of Measurements.

### 1. Qualitative Data (Categorical)

#### **a. Nominal Data:**&#x20;

Nominal data represents categories with no inherent order or ranking. Examples include:

* Colors (e.g. red, orange, yellow, green, blue, indigo, violet)
* Gender (e.g. male, female)
* Marital Status (e.g. single, married)
* Car makes (e.g. toyota, honda, ford, tesla)
* Zipcodes ( e.g. 10001, 94074)

**b. Ordinal Data:**&#x20;

Ordinal data signifies categories with a meaningful order, but the differences between them are not consistent or measurable. Examples include:

* Education levels (e.g., high school, bachelor's, master's)
* Customer satisfaction ratings (e.g., poor, satisfactory, excellent)
* Contractual membership levels (e.g. basic, pro, elite)

####

### 2. Quantitative Data (Numeric)

**a. Ratio Data:**&#x20;

In ratio data, there **is a true zero point**, and ratios are meaningful. This means you can say that one value is "twice" or "three times" another. Examples include&#x20;

* Weight of an object
* Income
* Annual sales&#x20;
* Market share

**b. Interval Data:**&#x20;

In interval data, there is **no true zero point**, and while differences between values are meaningful, ratios are not. For instance, a value of zero degrees Celsius or Fahrenheit does not signify the absence of temperature.

* Temperature in degrees Celsius or Fahrenheit
* SAT score (200-800)
* Credit score (300-850)

## Representation of Data

Common tools/ways to represent&#x20;

* Categorical Data:
  * Frequency Distribution Tables
  * Bar Charts
  * Pie Charts
  * Pareto Diagrams (Bar Chart + Cumulative Distribution Curve)
  * Cross (Contingency) Tables (for 2 Variables)
* Numeric Data:
  * Histograms
  * Scatter Plots (for 2 Variables)
