Types of Data in Statistics

Types of Data

There are three types of data:

– Numeric data: This is data that can be represented as a number, such as the number of students in a classroom.

– Categorical data: This is data that can be divided into categories, such as the gender of students in a classroom.

– Text data: This is data that is composed of text, such as a list of students’ names.

Classification of Data in Statistics

There are three types of data in statistics: categorical, quantitative, and qualitative.

Categorical data is data that is divided into categories. For example, if you asked someone their age, that would be categorical data because it is divided into categories (young, middle-aged, and old).

Quantitative data is data that is measured numerically. For example, if you asked someone how many brothers and sisters they have, that would be quantitative data.

Qualitative data is data that is not measured numerically. For example, if you asked someone what their favorite color is, that would be qualitative data.

Introduction to Types of Data

There are three types of data:

1. Nominal

2. Ordinal

3. Ratio

1. Nominal data are data that are simply listed in a sequence with no order. There is no intrinsic meaning to the ordering of the data.

2. Ordinal data are data that are ordered, but the distance between the values is not meaningful.

3. Ratio data are data that are ordered and the distance between the values is meaningful.

Qualitative or Categorical Data

Qualitative data is data that is not numerical. This type of data is typically represented by text or images. Categorical data is a type of qualitative data that is organized into groups.

Nominal Data

Gender

Male

Female

Age

18

21

Location

Boston

Chicago

Income

$50,000

$35,000

Occupation

Software Engineer

Accountant

Ordinal Data

Ordinal data are data that are measured in rank order. Examples of ordinal data include test scores, grades, and rankings.

The rank order of the data can be important, but the distances between the ranks are not. For example, if someone gets a score of 95 on a test, that is better than someone who gets a score of 85, but we cannot say that the difference between the two scores is 10 points.

Another example of ordinal data is the ranking of students in a class. We can say that one student is ranked first, another is ranked second, and so on, but we cannot say that the difference between the ranks is the same for all students.

Numerical or Quantitative Data

Numerical data is information that can be expressed in numerical terms. Quantitative data is a subset of numerical data that can be measured and compared.

Some examples of numerical data are:

-The number of people in a room
-The temperature
-The time

Some examples of quantitative data are:

-The number of people in a room at a given time
-The temperature at a given time
-The amount of money someone has

Discrete Data

A discrete data set is a data set that consists of a finite or countable number of values.

A simple example of a discrete data set would be the set of numbers {1, 2, 3, 4, 5}. This set has five values, and can be counted. Another example of a discrete data set would be the set of letters in the alphabet. This set has 26 values, one for each letter.

Discrete data sets are often used in statistics, because they are easier to work with than continuous data sets.

Continuous Data

The table below provides statistics on the number of people in the United States who identify as Asian American, Pacific Islander, or Native Hawaiian.

Asian American, Pacific Islander, or Native Hawaiian Population in the United States (in millions)

Year Asian American Pacific Islander Native Hawaiian 2008 14.8 0.5 0.2 2009 15.1 0.5 0.2 2010 15.4 0.5 0.2 2011 15.7 0.5 0.2 2012 16.0 0.5 0.2 2013 16.3 0.5 0.2 2014 16.6 0.5 0.2 2015 16.9 0.5 0.2

Source: http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_15_1YR_S1601&prodType=table

Ratio Data

The table below shows the percentage of people in each age group who report that they are in excellent or very good health.

Age Group Excellent or Very Good Health 18-24 years old 71% 25-34 years old 67% 35-44 years old 63% 45-54 years old 58% 55-64 years old 53% 65 years and older 47%

From the table, it appears that people in the 18-24 age group are the most likely to report that they are in excellent or very good health.