Linear Regression | What are the assumptions of Linear Regression?
Question
TheDataMonk
Follow Me
About TheDataMonkGrand Master
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 :)Follow Me
Answer ( 1 )
1) Linear Regression needs the relationship between the independent and
dependent variables to be linear. It is also important to check for outliers
since linear regression is sensitive to outliers.
2) Linear Regression needs all variables to be normally distributed. If the data
is not normally distributed, a non-linear transformation like the log-transform
might fix the issue.
3) It assumes that there is little or no multi-collinearity in data. It occurs when
the independent variables are too highly correlated with each other.
4) It requires that there is little or no autocorrelation in the data. Autocorrelation
occurs when the residuals are not independent from each other. In other words,
the value of y(x+1) is not independent from the value of y(x).
5) The residuals should be equal across the regression line.