| ,Strategy of improving the goodness of fit of the regression model(Ⅱ) ——the transformation of the arithmetic mean and the other variable transformations[J].SICHUAN MENTAL HEALTH,2019,32(1):9-15 |
| Strategy of improving the goodness of fit of the regression model(Ⅱ) ——the transformation of the arithmetic mean and the other variable transformations |
DOI：10.11886/j.issn.1007-3256.2019.01.002 |
| English keywords:Variable transformation Transformation of the arithmetic mean Logistic transformation Derived variable Goodness of fit |
| Fund projects:国家高技术研究发展计划课题资助(2015AA020102) |
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| The purpose of this paper was to introduce the second strategy of improving the goodness of fit of the regression models, the transformation of the arithmetic mean and the other variable transformations. The concrete approaches were as follows:①“ The transformation of the arithmetic mean” was adopted to the multi - value nominal independent variable. ②The derived variables were introduced to the quantitative independent variables, such as the results of “ logarithmic transformation ” “ square root transformation” “ exponential transformation” “ square transformation” “ cubic transformation” and “ cross product terms transformation” .③“ Logarithmic transformation” “ square root transformation” “ exponential transformation” “ reciprocal transformation” and “ Logistic transformation” were adopted to the quantitative dependent variable, respectively. ④ During building the regression models, the “ forward selection” “ backward selection” and “ stepwise selection” were used for screening the independent variables under the conditions both with the intercept term and without it. The several conclusions were achieved as below: ①The goodness of fit of the regression models was very low when no transformations was applied to the quantitative dependent variable and independent variables. ②The distinct results of the goodness of fit of the regression models could be achieved by using the distinct transformations to the quantitative dependent variable in accordance with the data conditions. ③ It was rational to transform the multi - value nominal independent variable by using the arithmetic mean transformation, which was conducive to improving the goodness of fit of the regression models. ④ It was wonderful to introduce the derived variables to the quantitative independent variables in fitting the regression models. ⑤It was helpful to improve the goodness of fit of the regression models by getting rid of the intercept term. |
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