Answer / raj

Regression is used to model Interval target variables where
as logistic regression is used to model Categorical
varaibles.

Regression Assumptions: Input variables are linearly
related to target variable and that input variables and
residuals are normally distributed, and independent with a
mean of 0 and constant variance.

Logistic Regression: There are no distributional
requirements for inputs in Logistic regression

Answer / vipin choudhary

Regression Modelling is also called linear modelling as the
input variables and residuals normally distributed.
the formula generally used here is
Y = m1x1+m2x2+m3x3+m4x4+c

Where as the logistic modelling is a non linear modelling
in which input variables and residuals randomly distributed.
The dependent variable has a value of either 1 or 0
the formula we use here is
Y= 1
--------------------
1 + e^-(m1x1+m2x2+m3x3+m4x4)

Answer / gaurav

Regression is a statistical technique to investigate relationship between dependent variable and independent variables which can be linear, nonlinear.

logistic regression is statistical technique to investigate relationship between ordinal dependent variable and independent variables. logistics regression can be multinomial if there are more then two ordinal dependent variable.

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