Category: Types of Regression Analysis

Tobit Regression is used to Evaluate linear relationships between variables when censoring ( observing independent variable for all observation) exists in the dependent variable. The value of the dependent is reported as a single value.

Cox Regression is useful for obtaining time-to-event data. It shows the effect of variables on time for a specific period. Cox Regression is also known as proportional Hazards Regression.

Quasi Poisson Regression is a substitute for negative Binomial regression. The technique can be used for overdispersed count data.

Similar to Poisson regression, negative Binomial regression also accord with count data, the only difference is that the Negative Binomial regression does not predict the distribution of count that has variance equal to its mean.

Poisson Regression is used to foreshow the number of calls related to a particular product on customer care. Poisson regression is used when the dependent variable has a calculation. Poisson regression is also known as the log-linear model when it is used to model contingency tablets. Its dependent variable y has Poisson distribution.

Ordinal regression is used to foreshow ranked values. The technique is useful when the dependent variable is ordinal. Two examples of Ordinal regression are Ordered Logit and ordered probit.

Support vector regression can be used to solve both linear and nonlinear models. Support vector regression has been determined to be productive to be an effective real-value function estimation.

It is a substitute technique of principal components regression when one has a widely correlated independent variable. The technique is helpful when one has many independent variables. Partial least regression is widely used in the chemical, drug, food, and plastic industry.

Principle components regression technique which is broadly used when one has various independent variables. The technique is used for assuming the unknown regression coefficient in a standard linear regression model. The technique is divided into two steps, 

1. Obtaining the principal components

2. Go through the regression Analysis on Principle components.

Elastic net regression is favoured over ridge and lasso regression when one has to deal with exceedingly correlated independent variables.