Regression Diagnostic Checking for Survey Data on Mothers’ Weights and Ages as Factors Responsible for Babies’ Weight at Birth

Abstract

When a regression model is considered for an application, we can usually not be certain in advance that the model is appropriate for that application, any one, or several, of the features of the model, such as linearity of the regression function or normality of the error terms, may not be appropriate for the particular data at hand. Hence, the diagnostic checking techniques on the regression model are essential. This study therefore is on model diagnostic checking techniques with application to linear regression analysis. In this study, a method useful for diagnosing violation of basic regression assumptions are presented and tested using a secondary data on babies’ weight at birth which serves as dependent variable and mothers’ weight and ages as independent variables. All the assumptions tested from the objectives (Normality of residual, collinearity between the independent variable, outlier/leverage, and linearity of the model) are met and no one deviated from the assumptions of multiple linear regression fitted on the data.