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    Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling
    (Universidade Federal de Minas Gerais, 2021) Genç, Serdar; Mendeş, Mehmet
    This study was carried out for two purposes: comparing performances of Regression Tree and Automatic Linear Modeling and determining optimum sample size for these methods under different experimental conditions. A comprehensive Monte Carlo Simulation Study was designed for these purposes. Results of simulation study showed that percentage of explained variation estimates of both Regression Tree and Automatic Linear Modeling was influenced by sample size, number of variables, and structure of variance-covariance matrix. Automatic Linear Modeling had higher performance than Regression Tree under all experimental conditions. It was concluded that the Regression Tree required much larger samples to make stable estimates when comparing to Automatic Linear Modeling
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    Linear modeling analysis using for determining the factors affecting 305-day milk yield
    (Universidade Federal de Minas Gerais, 2021) Genç, Serdar; Mendeş, Mehmet
    The purpose of this study was to model the factors affecting the 305-day milk yield of dairy cows by using Automatic Linear Modeling Technique (ALM). The data set of this study consisted of eight different cow breeds grown in eight province of Turkey. Results of ALM showed that the accuracy of the model was 64.2 % means that 64.2% of the variation in the 305-day milk yield could be explained by the constructed model. Created model was consisted of four factors namely the Breed, Lactation Length, Parity, and Province. Therefore, those selected factors were more efficient than the others in predicting the 305-day milk yield