For linear regression, what are some of the assumptions a data scientist is most likely to make?
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Answer / Arun Yadav
Some common assumptions in linear regression include: (1) Linearity: The relationship between the dependent and independent variables can be approximated by a straight line. (2) Homoscedasticity: The variance of the errors is constant across all levels of the independent variable(s). (3) Independence: The errors are not autocorrelated or influenced by each other. (4) Normality: The errors are normally distributed.
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