Statistical Analysis And Scientific Research

Gain practical experience in R for Data Analysis, Machine Learning and Artificial Intelligence. Become a Data Scientist.

This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.

Critically Analyze Research and Results Using R. Learn to recognize sloppy science, perform solid research and do appropriate data analysis.

Learn data science with R programming and Python. Use NumPy, Pandas to manipulate the data and produce outcomes | R

With the evolution in data storage, large databases have stimulated researchers from many areas, especially machine learning and statistics, to adopt and develop new techniques for data analysis in different fields of science. In particular, there have been notable successes in the use of statistical, computational, and machine learning techniques to discover scientific knowledge in the fields of biology, chemistry, physics, and astronomy. With the recent advances in ontologies and knowledge representation, automated scientific discovery (ASD) has further, great prospects in the future.

It has been evident from many years of research work in the geohydrologic sciences that a summary of relevant past work, present work, and needed future work in multivariate statistics with geohydrologic applications is not only desirable, but is necessary.

Lowers the Learning Curve for Physicians and Researchers!
The successful Statistical Reasoning in Medicine: The Intuitive P-value Primer, with its novel emphasis on patient and community protection, illustrated the correct use of statistics in health care research for healthcare workers. Through clear explanations and examples, this book provided the non-mathematician with a foundation for understanding the underlying statistical reasoning process in clinical research, the core principles of research design, and the correct use of statistical inference and p-values.

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed.

Critically Analyze Research and Results Using R. Learn to recognize sloppy science, perform solid research and do appropriate data analysis.