Home Forums SAS Programming Talks Assumptions of Linear regression with SAS

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    blogger rajeev
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    Let us discuss Assumptions of Linear regression. It is very very important question and will always be asked during Interview.

    Linearity – the relationships between the predictors and the outcome variable should be linear

    Normality & Independency– the errors should be normally distributed – technically normality is necessary only for the t-tests to be valid, estimation of the coefficients only requires that the errors be identically and independently distributed

    Homogeneity of variance (homoscedasticity) – the error variance should be constant
    Independence – the errors associated with one observation are not correlated with the errors of any other observation

    Errors in variables – predictor variables are measured without error

    Model specification – the model should be properly specified (including all relevant variables, and excluding irrelevant variables)

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