types of categorical variables

Categorical variables represent groupings of things (e.g. This is a guide to Types of Variables in Statistics. The variable which is similar to an independent variable is called a covariate variable but is impacted by the dependent variable but not as common as a variable of interest. Here the independent variables are IQ and estimated time which may or may not reflect in the productivity of an employee. Examples: sex, business type, eye colour, religion and brand. Example : Consider the dataset as shown below which has a Categorical feature fuel_injection_type with following values TSI, TDI, TGI. which means if the feature fuel_injection_type has 100 distinct values then One Hot Encoder creates 100 different columns where only few columns are 1's and the rest becomes 0's, which is very sparse in nature. There are a number of typologies but one that has proven useful is provided in the following table. Each type of data has unique attributes. Label Encoder: This Encoder transforms the Categorical feature into an arbitrary Numerical feature, it starts assigning numbers starting from 0 till number of distinct classes present in that particular feature. win or lose). Categorical variables are similar to ordinal variables as they both have specific categories that describe them.

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For example, A manager asks 100 employees to complete a project. So the extension of estimated time or enhancing the IQ of a person doesn’t make any sense in employee’s productivity as it is not predictable.
The first reason can be some will be working hard for day and night to complete the project within the estimated time and the other one is some guys are born intelligent and smarter than others. ALL RIGHTS RESERVED. You learned about this difference when you read about data types and variables in Chapter 2, "Understanding and Organizing Business Data." For nominal variables with more than two categories the order does not matter. A variable can occurs in any form such as trait, factor or a statement that will be constantly changing according to the changes in the applied environment. Nominal: represent group names (e.g.

Since Excellent > Good > Satisfactory > Bad > Horrible the ratings also shall be assigned in the following order, else the results might become difficult to comprehend. This categorical data encoding method transforms the categorical variable into a set of binary variables (also known as dummy variables).

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