There are mainly three types of regression:
Regression is a vast topic and we all have a little less knowledge about it. Once you explore the regression you will get to know its capabilities to deal with different types of variables. Logistic regression lets you deal with multi-level dependent variables.
Ordinal logistic regression is actually a generalisation of multiple linear regression. It is used to find interactions between independent variables so that dependent variables can be predicted. This type of regression is used to predict the dependent variable with ‘ordered’ multiple categories and independent variables. Thus it facilitates the interaction of dependent with one or more independent variables. It predicts an ordinal dependent variable given one or more independent variables. Ordinal regression is also being used to determine the interactions between independent variables to predict the dependent variable. For example, a survey is done, in which there was a question on which the response lies between agreeing and disagree. This data doesn’t help in generalising well. Hence the level of responses further gets added as Strongly Disagree, Disagree, Agree, Strongly Agree. This offers a clear picture and is more realistic.
While using the ordinal regression to analyse your data, just make sure that your data "passes" the four assumptions mentioned below to give you a valid result. Also, make sure the test is conducted in this order only as any breach and you will no longer be able to use ordinal regression.
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