In this study, older age, male gender, low BMI, smoking and chronic sun exposure had a negative influence on skin ageing in a Turkish population.

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Increasing population and the rising air temperatures are known as factors that cause water depletion in the watersheds. Therefore, it is important to accurately predict the future ratios of tap water consumers using the same watershed to the population living in the specified area, to produce better water policies and to take the necessary measures. Predictions can be made by a growth curve model (GCM). Parameter estimations of the GCM are usually based on the ordinary least square (OLS) estimator. However, the outlier presence affects the estimations and the predictions, which are obtained by using the estimated model. The present article attempts to construct first- and third-order GCMs with robust least median square (LMS) and M estimators to make short-term predictions of ratios of tap water consumers. According to the findings, parameter estimations of the models, the outliers, and the predictions vary with respect to the estimators. The M estimator for short-term predictions is suggested for use, due to its robustness against outlier points.

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In multiple linear regression analysis, the variance inflation factor is a well-known collinearity measure. It is defined as the function of the coefficient of determination between the explanatory variables, and it is based on the maximum likelihood estimator of the regression coefficients. Nevertheless, in addition to outliers, leverage observations can have significant impact on the coefficient of determination, and thereby the variance inflation factor. This study presents an improved robust variance inflation factor estimator that is not affected by these observations. Simulation studies and a real data analysis indicate that the modified robust variance inflation factor estimator performs better than the traditional one.