**Linear Regression**is used to establish a relationship between Dependent and Indipendent variables, which is useful in estimating the resultant dependent variable in case indipendent variable change. For example -

Using a Linear Regression, the relationship between Rain (R) and Umbrella Sales (U) is found to be - U = 2R + 5000

This equation says that for every 1mm of Rain, there is a demand for 5002 umbrellas. So, using Simple Regression, you can estimate the value of your variable.

**Logistic Regression**on the other hand is used to ascertain the probability of an event. And this event is captured in binary format, i.e. 0 or 1.

Example - I want to ascertain if a customer will buy my product or not. For this, I would run a Logistic Regression on the (relevant) data and my dependent variable would be a binary variable (1=Yes; 0=No).

In terms of graphical representation, Linear Regression gives a linear line as an output, once the values are plotted on the graph. Whereas, the logistic regression gives an S-shaped line.

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