![]() ![]() Many graphical methods and numerical tests have been developed over the years for Related, can cause problems in estimating the regression coefficients. Collinearity – predictors that are highly collinear, i.e., linearly. ![]() Influence – individual observations that exert undue influence on the coefficients.Speaking are not assumptions of regression, are none the less, of great concern to Variables, and excluding irrelevant variables)Īdditionally, there are issues that can arise during the analysis that, while Model specification – the model should be properly specified (including all relevant.Errors in variables – predictor variables are measured without error (we will cover this.Independence – the errors associated with one observation are not correlated with the. ![]() Homogeneity of variance (homoscedasticity) – the error variance should be constant.That the errors be identically and independently distributed Necessary only for hypothesis tests to be valid,Įstimation of the coefficients only requires Normality – the errors should be normally distributed – technically normality is.Linearity – the relationships between the predictors and the outcome variable should be.This chapter will explore how you can use Stata to check on how well yourĭata meet the assumptions of OLS regression. Without verifying that your data have met the assumptions underlying OLS regression, your results mayīe misleading. STATA REGRESS IF NOT HOW TOIn the previous chapter, we learned how to do ordinary linear regression with Stata,Ĭoncluding with methods for examining the distribution of our variables. ![]()
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