Question

what is the relationship between linear, multiple and logistic regrerssion

Answer 1

Linear regression is a method of modeling that involves the continuous dependent variable(response) and the independent variables(or explanatory variable) can be continuous or discrete. By using best fit straight line linear regression sets up a relationship between response variable (Y) and one explantory (X). In other words, there exist a linear relationship between independent and dependent variables.

The general form : y = bx + c

Now, Multiple regression is a similar modeling method which involved more than one independent variables( or explanatory variable) for modeling the response variable.

The general form : y = c+ b1x1 + b2x2 + + b3x3 ....

The logistic regression technique involves dependent variable which can be represented in the binary (0 or 1, true or false, yes or no) values, means that the outcome could only be in either one form of two. For example, it can be utilized when we need to find the probability of successful or fail event. Here, the same formula is used with the additional sigmoid function, and the value of Y ranges from 0 to 1.

The general form :

The contrasting features of Linear or multiple regression and Logistic Regression

1. The Linear regression models data using continuous numeric value. As against, logistic regression models the data in the binary values.
2. Linear regression requires to establish the linear relationship among dependent and independent variable whereas it is not necessary for logistic regression.
3. In the linear regression, the independent variable can be correlated with each other. On the contrary, in the logistic regression, the variable must not be correlated with each other.