Linear Regression | What are the assumptions of Linear Regression?

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LR

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TheDataMonk 55 years 1 Answer 1170 views Grand Master 0

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  1. swap007
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    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.

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