Samuel Berestizhevsky

Statistics is an essential discipline for health sciences. However, studying statistics often presents challenges for students and practitioners. Some approaches that are used to overcome challenges include simplifying statistical concepts, providing step-by-step instructions for applying statistical methods and utilizing interactive statistical software. While these approaches ease the study of statistics and help students pass exams, they also perpetuate a lack of comprehension of statistical concepts. This can lead to the misuse of statistical methods and the misinterpretation of results, underscoring the importance of a solid understanding of statistical concepts. This book should be considered a supplementary resource for statistical courses. It demystifies specific statistical concepts by addressing commonly asked questions, making the learning process more engaging and less intimidating. It encourages a more critical approach to statistical analysis and teaches readers how to apply statistical techniques to real-life problems in the biomedical and health science areas. The book also provides examples of meaningful analysis with statistical software (R, SAS, SPSS, Stata).

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn’t ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn’t ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub