# Data & Variables

- Posted by Data Science Anywhere
- Categories Statistics
- Tags Data, Statistics

## Variables

There are two types of variables those are

- Independent variables
- Dependent variable: This variables is also call target variables or class variable. One of the assumption is that dependent variable depends upon
**independent variable**.

## Data

Data is furthered classified into two types Categorical and Numerical. The following tree diagram shown below.

**Examples of Qualitative Data:** Consider the image of the right hand side. The categorical information or descriptive information are :

- He is brown
- He has long hair
- He has lots of energy

**Example of Quantitative Data or Information: **

*Discrete*- He has 4 legs
- He has 2 brothers

**Continuous:**- He weighs 25.5 Kilograms
- He is 565 mm tall

### Numerical vs Categorical Data:

Although it is self explanatory regarding numerical and categorial data. But sometimes categorical data can also represented in numerical form.

Eg: Employee ID, Voter ID, ZIP or PIN Code.

Employee id is in numerical form but the number represents name of person. Similarly zip code or pin code represent the place etc.

By keeping above points in mind we can split categorical and numerical into 2 parts each and those are displayed below.

- Categorical data further split into
**Nominal and Ordinal data**

- Numerical data further split into I
**nterval and Ratio data**

### Nominal Data:

Nominal means name and count. They are the categories** without order or direction.**

- Date are alphabetic or numerical in name
- There kind of data is used to track people, object or events.

### Ordinal Data:

Ordinal means rank or order. Data place in order like ranking or scaling then such data is call ordinal data.

- This data has no
**absolute**value. - More precise comparison are not possible.

### Nominal vs Ordinal Data:

### Interval Data

Data where ordering is clear and the difference in data values is meaningful.

Interval data, also called an integer, is defined as a data type which is measured along a scale, in which each point is placed at equal distance from one another.

- However, there is
**no natural zero or origin.**

Example: Year 1008 vs 2016 Temperature: 14C vs 28C.

### Ratio Data

Ratio level data is similar to Interval level data, with the key difference

- There is a
**natural zero point.**

Examples: Weights, Cost of things, Number of correct answers in a exam

### Summary

### Discrete & Continuous

Discrete: This is finite value and can be countable.

Continuous: We cannot measure this continuous value. Generally we specify some range or approximate value.

Example:

-x-

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Tag:Data, Statistics