Parametric regression models assume that the dependent variable is a linear relationship with the independent variables and the form of the relationship is known. Nonparametric regression methods are applied in cases where the relationship type is not known or assumptions cannot be provided. However, when there is more than one independent variable, some of the independent variables may be in a linear relationship with the dependent variable, while some may be in a nonlinear relationship. In order to model these variables, semiparametric regression models, which are a combination of parametric and nonparametric regression methods, are used. In this study parametric, nonparametric and semiparametric regression models, parametric estimates, fit statistical values of the models, confidence intervals and standard error values were calculated. As a result of the analysis, the parameters of the milking unit and the quarantine area among the parametric variables, the operation area, the ventilation area, the number of ventilation, the quarantine area, the infirmary area, the manure pit and the distance to the center among the non-parametric variables were found to be statistically very important (P
Regression analysis is known as statistical methods applied to model and analyze the relationship between variables. Regression method can be examined as parametric, non-parametric and semiparametric regression methods. The parametric regression method assumes that the dependent variable is in a linear relationship with the independent variables and that the shape of the relationship is known. If these assumptions are not met, non-parametric regression methods are applied. However, these methods cause difficulties especially in the interpretation part due to the problem of multidimensionality when there is more than one independent variable. Thus, when there is more than one independent variable, some of the independent variables may be in a linear relationship with the dependent variable, while the other part may be in a nonlinear relationship. Thus, in order to model these relationships, semiparametric regression methods, which are the additive combination of parametric and non-parametric regression methods, are used. In this study, parametric regression method, definition of non-parametric regression method and assumption conditions are given. It has been shown that the semiparametric regression method can be applied in cases where these assumptions are not met. Thus, in the study, regression methods were examined in three different parts, and parametric, non-parametric and semiparametric regression methods were examined theoretically.
In this article, the 19-item information dimension of the scale, originally named "Sustainability Consciousness Questionnaire", was developed by Michalos, Creech, Swayze, Kahlke, Buckler & Rempel (2012) and updated by Gericke, Pauw, Berlung & Olsson (2018), whose factor structure was previously revealed. (latent variable) Construct validity was tested by applying it to a new data set of 307 people. IBM SPSS and AMOS statistical package programs were used in the analysis of the data. According to the results obtained in the study; It was found that the information dimension of the scale was gathered under three factors as in the original and it could explain 61.72% of the total variance. As a result of the reliability analysis, it was determined that the scale had a high level of reliability according to the Cronbach's-α coefficient (Cronbach's-α =0.923). In the confirmatory factor analysis, the improvement in the goodness of fit coefficients was examined by controlling the modification indices. In addition, the path coefficients of how much the 19 items belonging to the first level of the scale predict the latent variable are all significant. Among the items, it was found that the 13th and 14th items had the most effect (β1= 0.828, p
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