Step 1

Indicator variable: also known as the dummy variables are the variable that is numerical or quantitative and they represent the data that are categorical in nature such as gender, age, etc. these variables are the artificial variables added to show an attribute with distinct categories. these variables used to take the value of 0 or 1 only for indicating the presence or absence of some categorical data that can affect the expected outcome shift.

Step 2

Indicator variables are used in regression analysis as the practitioners used the data of categorical in nature in the model building by using linear regression analysis. This analysis involved the different investigating variables like location, colors. when the model is using the multiple or single linear regression analysis and the variables are being explained in various continuous factors or categorical inputs. Regression analysis used to fit the relation by typically way that is minimizing the square error sum and the value observed. this process is called OLS (ordinary least square). this method is based on categorical data and for fitting the relation sometimes the indicator variable is used.

Indicator variable: also known as the dummy variables are the variable that is numerical or quantitative and they represent the data that are categorical in nature such as gender, age, etc. these variables are the artificial variables added to show an attribute with distinct categories. these variables used to take the value of 0 or 1 only for indicating the presence or absence of some categorical data that can affect the expected outcome shift.

Step 2

Indicator variables are used in regression analysis as the practitioners used the data of categorical in nature in the model building by using linear regression analysis. This analysis involved the different investigating variables like location, colors. when the model is using the multiple or single linear regression analysis and the variables are being explained in various continuous factors or categorical inputs. Regression analysis used to fit the relation by typically way that is minimizing the square error sum and the value observed. this process is called OLS (ordinary least square). this method is based on categorical data and for fitting the relation sometimes the indicator variable is used.