Dependent Variable is also known as an Output variable, Target variable, Y variable
Independent Variable is also known as Explanatory Variable, Input Variable, X variable.


ANOVA - is used when we want to see the difference between the means of two groups. As you know in statistics, groups are known as a category. So here our input variables are categories and the output variable will be continuous.

ANCOVA - is used when our Y variable is a continuous variable

And in the independent(x) variable, we should have at least one Continuous variable and at least one categorical variable.



Linear Regression - is used when our Y variable and X variable both are numeric. Based on the X value the model will try to predict the Y (target).



Multiple Regression - Multiple Regression is an extension of Linear Regression. When we want to predict the Y variable, that depends on multiple X variables then multiple Regression is used.



Logistic Regression -is used when the output is categorical and input could be anything. Common uses are where the dependent value is binary ie the output can be only two things. e:g male:female, true:false or counts.


Logistic Regression is a classification algorithm. So why it is named a Regression?


- As the primary decision made by this algorithm is continuous (regression) and then a final tuning (conversion - mostly using a sigmoid function) convert this continuous output to a categorical output. That's why although it is named as regression, it is a classification algorithm.