Linear regression assumptions

Question

Accepted Answer

Linear regression assumptions include linearity, independence, homoscedasticity, and normality.

Assumption of linearity: The relationship between the independent and dependent variables is linear.
Assumption of independence: The residuals are independent of each other.
Assumption of homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.
Assumption of normality: The residuals are normally distributed.
Example: In a study examining the relationship between study hours and exam scores, the assumptions of linear regression would include linearity, independence, homoscedasticity, and normality.

Accepted Answer

Linear Model
No Multicolinearlity in the data
Homoscedasticity of Residuals or Equal Variances
No Autocorrelation in residuals
Number of observations Greater than the number of predictors
Each observation is unique
Predictors are distributed Normally

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Linear regression assumptions

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