Difference Between Linear & Logistic Regression — A Common Data Scientist Interview Question

Having attended numerous data scientist job interviews, I was asked this particular question 75% of the time:

Can you tell me the key differences between Linear Regression and Logistic Regression?

To be honest, you can easily google the answer to this question as it’s really common in the world of data science, but I thought I should try writing a post to discuss the differences and list them down in order of importance so that you can just quote the few most important ones, we all know how stressful it is to prepare for a job interview, much more remembering the concepts and theories.

First thing first, what is Linear Regression? Let’s take for example this problem statement, “** higher paid employees tend to spend more in their daily expenditure**” Logically, we will expect the answer to be yes. Can we try to explain this relationship with some mathematical model? The answer is Linear Regression.

Linear Regression is a statistical method that allows us to predict a quantitative response Y (i.e. amount spent on daily expenditure) based on the basis of a predictor variable X (i.e. salary drawn by employee). It assumes the relationship between Y and X to be linear, which is a straight line that follows this equation:

data-science interview data-scientist linear-regression logistic-regression

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Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding.

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