In this study, we compared the Student's t-test, Welch's t-test, and Mann-Whitney U test, in terms of their type I error rate and statistical power when the assumptions of parametric tests are violated in different situations. Materials used in this study, consisted of random numbers generated using the Numpy library in the Python programming language. All random numbers were generated from a normal distribution with N (0, 1) parameters. Balanced and unbalanced experimental conditions were simulated 50 000 times for each combination. The study revealed that, in comparison to other tests, Welch’s t - test was particularly more conservative in terms of type I error rate. It was discovered that the Student-t test had higher power values than the Mann-Whitney U test, mainly when only a small sample size of observations was used for the analysis. This simulation study indicated that Welch’s t - test is robust for preserving type I error rate when the distribution is normal. Therefore, in practice, the use of Welch t-test is recommended based on the findings of this study. One of the recommendations of this study is that the tests in question should also be evaluated in cases where observations have different distributions.

Due to the development of computing technology and different machine learning models, big data sets have gained importance in animal science as well as in many disciplines. The main objective of this study was to compare different machine learning algorithms to predict daily dry matter intake (DMI) in feedlot cattle. The data consisted of 2660 cattle pens placed on feed between January 1988 and December 1997. Machine learning methods were compared in heifers and steers, with 718 in pens of heifers and 1942 in pens of steers. Initial body weight, days on feed, and average proportion of dietary concentrate were used as independent variables to predict DMI in steers and heifers separately. The multivariate linear regression (LR), random forest (RF), gradient boosting regressor (GBR), and light gradient boosting machine (LGBR) algorithms were compared in terms of several performance metrics (MAE, MAPE, MSE, and RMSE). Results showed that the determination coefficient alone is not a good single criterion. It is recommended that the interpretation of model consistency should also consider MAE, MAPE, MSE, and RMSE values. In the current study, all machine learning algorithms yielded similar and lower performance metrics. However, the LGBR and GBR algorithms, were found to perform slightly better than the other algorithms, especially in heifers. Increasing the number of animals and using different independent variables that are related to the DMI can affect the accuracy of DMI prediction.

In this study, the rotation stage of factor analysis, which is one of the multivariate analysis methods, was examined. All stages of factor analysis have been defined. The material of the study consisted of a data set obtained from barley planted in 20 plots (replication) having 9 variables. In each plot, the average of 6 plants selected from that plot was used. The variables emphasized in the study were plant height, number of leaves, spike length, spike weight, grain yield, flowering period (days), harvest index, yield, and 1000-grain weight. Factors were obtained by principal component analysis, which is a factor extraction method, from the data set that met the prerequisites of the analysis. The criteria used in different factor rotations are given and based on these criteria, the formula that gives the optimum rotation angle for each data set was obtained. As a result, the formulas obtained for orthomax, varimax, quartimax, and equamax were applied to the factors obtained from the data set and the results were interpreted. As a result of factor rotation, when varimax, quartimax, and equamax methods were used, the values of the variables in terms of factor loads differed in each factor. This is a desirable situation for factor analysis results.

In order to give detailed information about the characteristics of scientific studies related to the power of the test, a bibliometric analysis of studies in which the power of the tests used in statistical data analysis was determined with the aid of Monte Carlo simulation techniques was carried out. The Web of Science (WoS) database's 1309 scientific studies in which the power of the test was determined by using Monte Carlo simulation techniques served as the study's material of data. The analysis includes the number of studies by year, average citation count by year, the number of articles published in scientific journals, countries of responsible authors, and the most relevant words in the studies. It was observed that Communications in Statistics-Simulation and Computation and Journal of Statistical Computation and Simulation are the journals with the highest number of published articles. The United States of America (USA) takes the lead when considering the countries of corresponding authors, while Türkiye is in 8th place. The most used keywords in the ten-year time period of scientific studies were respectively “power”, “test”, “skewness”, “model” and “inference”. As a result, it can be concluded that test power studies can be obtained safely using the Monte Carlo simulation technique.

Bu çalışma, 1980 ile 2023 yılları arasında WOS veri tabanındaki 1455 çalışmadan elde edilen verilere dayanarak, çiftlik hayvanlarında laktasyon eğrisi modellemesinin bibliyometrik analizini sunmaktadır. Elde edilen sonuçlarda, 26 çalışmayla öne çıkan yazar Macicotta NPP ve çalışma sayısı açısından Cornell Üniversitesi önde gelen kurum olarak görülmüştür. Bu alanda en önemli dergi olarak Journal of Dairy Science, 393 makaleyle öne çıkmaktadır. Temsil edilen ülkeler arasında Türkiye, 44 çalışma ile 13. sırada yer almakta ve bu çalışmaların çoğunluğu yerel yazarlarla iş birliği içinde yürütülmektedir. Bu bulgular, laktasyon eğrileri alanında gelecekteki araştırma çalışmaları için değerli bir perspektif sunmakta ve ortak yazar seçimi, dergi seçimi ve ilgili anahtar kelimeler konusunda rehberlik sağlamaktadır. Çalışmanın sonucunda, çiftlik hayvanlarında laktasyon eğrilerinin kullanımı için farklı matematik modellerin kullanılması ve yaygınlaşması gerektiği düşünülmektedir.