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Regression Line vs Line of Best Fit

Regression Line vs Line of Best Fit,understand the difference between the two concepts of Linear Regression
Regression Line vs Line of Best Fit

The regression line (curve) consists of the expected values of a variable (Y) when given the values of an explanatory variable (X). In other words it is defined as E[Y|X = x]. To actually compute this line we need to know the joint distribution of X and Y, which in many cases we don’t know.

The line of best fit can be thought of as our estimate of the regression line. “Best fit” is not a precise term, since there are many ways to define it (ie using a least squares criterion, minimizing the absolute values of the residuals etc.).

One desirable property for the line of best fit to have is for it to converge to the regression line as our number of observations increase. And in the case of the variables X and Y having a bivariate normal distribution (which is often assumed) and selecting the ordinary least squares line as best fitted can be proven to occur.

The above answer was shared by Michael Zahir.

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Important Resources to crack interviews (Mostly Free)

There are a few things which might be very useful for your preparation

The Data Monk Youtube channel – Here you will get only those videos that are asked in interviews for Data Analysts, Data Scientists, Machine Learning Engineers, Business Intelligence Engineers, Analytics managers, etc.
Go through the watchlist which makes you uncomfortable:-

All the list of 200 videos
Complete Python Playlist for Data Science
Company-wise Data Science Interview Questions – Must Watch
All important Machine Learning Algorithm with code in Python
Complete Python Numpy Playlist
Complete Python Pandas Playlist
SQL Complete Playlist
Case Study and Guesstimates Complete Playlist
Complete Playlist of Statistics

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I am the Co-Founder of The Data Monk. I have a total of 6+ years of analytics experience 3+ years at Mu Sigma 2 years at OYO 1 year and counting at The Data Monk I am an active trader and a logically sarcastic idiot :)

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