Data Types in Statistics
What is Data?
We can say any piece of information is Data. Data is units of information in structured or unstructured format. Data available in different kind.
Examples: Collection of relevant tweets, Records of yield in a farm over a period, Records of stock price every minute, Records of performance of a sports person.
In general, there are four types of data.
1.Time Series Data
2. Cross sectional data
3. Pooled Data
4. Panel Data
Time series data: Time series data is the set of observations on a variable at different timepoints. It is a collection of quantities that are assembled over even intervals in time and ordered chronologically. The data may be collected daily, weekly, monthly. Below image shows sample time series data
Cross-sectional data: Cross-sectional data are set of observations on two or more variables at the same time point. This data analysis is when you analyze a data set at a fixed point in time. The datasets record observations of multiple variables at a particular point of time. Financial Analysts may, for example, want to compare the financial position of two companies at a specific point in time. To do so, they would compare the two companies’ balance sheets.
Pooled data: This is combination of both time series data and cross-sectional data. Pooled data occur when we have a “time series of cross sections,” but the observations in each cross section do not necessarily refer to the same unit.
Panel data: Panel data, referred to as longitudinal data, is data that contains observations about different cross sections across time. Panel data is type of pooled data. The same cross-sectional unit is surveyed over time. Also known as longitudinal or micro-panel data.
What Are Variables?
In statistics, a variable has two characteristics:
A variable is an attribute that describes a person, place, thing, or idea. The value of the variable can vary from one entity to another. To understand the statistics, we need to first understand the type of each variable or column Variable.
Numerical Variable
Numeric variables have values that describe a measurable quantity as a number. A variable which takes a numeric value is called a numeric variable. Also known as quantitative variable
A discrete numeric variable is a random variable which takes discrete values, i.e. values from the set of whole numbers only. It can take countably finite values
Examples: Number of cars passing by a toll-gate every one minute, Number of defective items in a box
A continuous numeric variable is a variable which can have infinite number of values within a range.
Examples: The amount of rainfall in millimeters, Price of a stock
Categorical Variable
Categorical variable has two or more levels. Also known as qualitative variable
Examples: Colors: red, orange, yellow. Gender: male, female. Economic status: low, medium, high
Nominal data has no order and has two or more than two categories. It represents discrete units and are used to label variables
Example: Housing type — Apartment, Bungalow, Penthouse
Binary data has no order and has strictly two categories. Also known as dichotomous variable
Example: Presence or absence of a disease
Ordinal data is ordered nominal data. It represents discrete and ordered units
Example: Education — Primary, Secondary, High School, College and University which are ordered
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