In this section we learn introduction Data and Variable in statistics, examples of Data and Variable, types Data and variable etc. Without killing time let’s begins
What are data in statistics?
- The word ‘data’ has derived from the Latin word ‘datum’ which means information. The information may be quantitative and qualitative.
- Data are aggregates of facts that can be expressed directly or indirectly in the numerical form. In other words, the data are sets of values expressed on one or more observational units.
- In fact, the data are the raw materials for the final statistical conclusions. They have prime importance in our daily life activities.
Nature of data:
- Quantitative.
i. Discrete.
ii. Continuous. - Qualitative.
i. Discrete.
What is Variable in statistics?
A variable is any characteristics, number or quantity that can be measured or counted. In other words, variable is any value or entity or characteristics which can vary or change across individuals, groups or objects. It is also called ‘variate’ The variables are usually denoted by capital letters and the values of the variables are denoted by small letters. Examples: Age, weight, income, temperature, eye color, taste, flavor etc.
Different type of Data and Variable in statistics?
Data can be classified on different basis. There are so many types/forms of data. However, the following are the major types of data:
❑ On the basis of nature:
- Quantitative data.
- Qualitative data.
❑ On basis of sources/collection procedure: - Primary data.
- Secondary data.
❑ On the basis of measurement scale:
i. Nominal data.
ii. Ordinal data.
iii. Interval data.
iv. Ratio data.
❑ Other types of data
i. Cross-section data.
ii. Time series data.
iii. Spatial data (geospatial data).
iv. Failure time data etc.
Examples of data
- Quantitative data: Price, quantity, age, marks, no. of bacteria, etc. related data.
- Qualitative(categorical data): Habit, religion, sex, beauty, intelligence, casts, prevalence etc related data.
- Primary data: census data, survey data, data from interview, questionnaire, focus-group discussion, experiments etc.
- Secondary data: data from reports, records, books, newspaper, magazine, etc.
- Nominal data: habit, religion, sex, disease etc.
- Ordinal data: beauty ranks, education levels, economic status etc.
- Interval data: temperature, sea level, etc related data.
- Ratio data: height, weight, length etc related data.
- Cross section data: nutritional survey data, food survey data, survey related to accidents, natural disaster etc.
- Time series data: daily, weekly, yearly production related data, census data etc.
- Spatial data: data related to area, space, regions etc.
- Failure time data: data obtained from clinical studies, cancer studies, survival analysis etc.
What are Types of variable:
- Quantitative variable (Numerical variable):
i. Discrete variable.
ii. Continuous variable. - Qualitative variable (categorical variable):
i. Discrete variable
Quantitative variable(Numeric variable) in statistics
The variables which can be expressed directly into the numerical form are called quantitative variables. Weight, height, quantity, marks, no. of people, no. of items etc are some examples of quantitative variables.
Discrete variable
A variable that takes only the exact or discrete or integer value is known as discrete variable. The common examples of discrete variables are no. of students, no. of packets, no. of buildings, no. of trees, no. of patients etc.
Continuous variable:
A variable that can take any numerical value within a certain range is called continuous variable. The common examples of continuous variables are weight, height, marks, quantity, price etc.
Qualitative variable (categorical variable) in statistics
The variables that can not be expressed directly into the numerical form are called qualitative variables. In other words, the variables that can be categorized only are called qualitative variables. Color, race, sex, taste, flavor, religion, habit etc are some common examples of categorical variables. Categorical variables are always in discrete.
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