This thesis study examined the statistical models and evaluation criteria used in poultry breeding, account considering live weight gain, egg weight and egg numbers. For this purpose, as an example of live weight increase; 9-week live weight gains of Ross-308 broiler line, as an example of egg weight; the average of 20-week egg weights of brown Lohmann laying hens and 9-week egg yields of Japanese quail were used as an example of the number of eggs. In modeling, for live weight gain; 10 different models for egg production; 11 different models and 8 different models for egg weight were considered. In evaluating the models; error mean squares, coefficient of determination, corrected coefficient of determination, Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, Accuracy Factor, Bias Factor and Durbin-Watson autocorrelation values were taken into consideration. As a result of the study, in terms of live weight gain; The Cubic Piecewise regression model is the best in terms of egg yields; It was determined that the Modified Compartmental model and the Logistic model gave better results than the others in terms of egg weight. The worst models are in live weight gains; Brody and egg yields; It was concluded that there was a Quadratic Linear and Von Bertalanffy model for egg weight.

Internal and external quality characters of poultry eggs are quitely important to determine egg weight. Also, the quality of eggs is important for both hatching and egg production. The purpose of this study was modelling of egg weight with the MARS (Multivariate Adaptive Regression Splines) method using inner and outsider quality characters of egg in Lohmann LSL Classic white hybrit flock. For this purpose, the eggs of the Lohmann LSL Classic white hybrid flock (n=60) were used. Weekly egg yields were evaluated from the 22nd week to the 62nd week. In the research, for the prediction of dependent and continuous variable egg weight; shape index (SI), shell breaking resistance (SBS), shell weight (SW), shell thickness (ST), yolk diameter (YD), yolk width (YW), yolk height (YH), color (YC ), albumen length (AW), albumen height (AL) and albumen height (AH) were used. In order to obtain perfect goodness of fit, in the “earth” package of the R program, the definitions of penalty -1, degree = 2, nprune = 10 and nk = 60. The research, the mars prediction model was determined such as EW = 63.1-0.906 * max (0,75-SI)-0.32 * max (0, SI-75) -62.4 * max (0,0.57-ST) -354 * max (0, ST-0.57) + 1.13 * Groupa2 * max (0, 75-SI) + 1.49 * (0.0.57-ST) max * YD + 8.2*max(0, ST 0.57) * YD-0.02*(0 YD-38.5)max* YC-0.0366*YH * max(0,13-YC). As a result, some quality variables were found to be important in determining egg weight. Variables such as group a2, SI, YC, ST, YD, YH to estimate the weight of the egg determined as the dependent variable were used. Other variables are not included in this equation. In the poultry, the MARS prediction model may be a better alternative to classical nonlinear models in predicting egg weight since that it is easier and has higher accuracy.

In this study, starting with the researches method and studies, which scale used and in accordance with this scale, which data statistical relationship measures to be applicated were given. In accordance with this aim, 18 tests were taken into consideration which consisted of statistical relationship measures tests. Also, data sets which were taken from2148 students from three different high schools in Kahramanmaraş were examined practically on statistical relationship measures. With the aim of this, knowledge that were taken from the students from Science High School and Vocational High School were used. In the study, the effects of the factors like gender, education status of the father, private room status, number of siblings, computer status, living place,taking private lesson status, age of the student, body weight of the student and the income of the family on the effect of high school type were evaluated with the statistical relationship measures. Parametric test were applied to the data sets which are intermittent scale or proportional scale. And non parametric testa were applied to the data sets which are classifier scale or sequential scale. As a result, in the thesis study, the effect of factors that were thought to effect the student’s high school type, effect degree and direction were tried to be determined with the relationship measures test.

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

This study was carried out to determine the most suitable model for egg weights of the Cubic, Gompertz, Logistics, Gamma, Richard, Piecewise Quadratic, Orscov, and Sigmoaidal models, which are widely used in estimation. Lohmann-Brown Classic laying hens raised in Ondokuz Mayıs University Research and Application farm were used as animal material. In the modeling of egg weights of 351 layer hens raised in 3-storey cages, individual egg weight data measured at weekly intervals, 32 on the 1st floor, 17 on the 2nd floor, and 27 on the 3rd floor, were taken into account, and a total of 76 hens' individual egg weight modeling was carried out. Coefficient of determination, mean square error, Durbin-Watson, and Akaike Information Criteria values were taken into account in modeling egg weights and comparing the compatibility of models with point distribution. According to the comparison criteria, it was determined that the best estimation model was Richard. It was determined that the closest predictions to the Richard prediction model were obtained from the Logistics and Gompertz models. In addition, it was concluded that Orskov, Sigmoidal, and Quadratic piecewise regression models had the worst fit.