R Programming

Dr. Kingsley Okoye received his Ph.D. in Software Engineering from the University of East London, UK, in 2017. He has worked as Data Architect and currently a Research Professor and Mentor at The Institute for Future of Education, Tecnologico de Monterrey and Department of Computer Science, School of Engineering and Sciences, Tecnologico de Monterrey, Mexico. He is a Senior Member of The Institute of Electrical and Electronics Engineers (IEEE). His research interests include Process Mining and Automation, Semantic Web Technologies, Learning Analytics and Systems Design, Data Science, Artificial Intelligence, Text Mining, Computer and Education, Educational Innovation, Educational Technologies, Knowledge Engineering and Data Management, Internet Applications and Ontology. Dr. Samira Hosseini obtained her Ph.D. in Biomedical Engineering from the University of Malaya, Kuala Lumpur, Malaysia. She served as a postdoctoral associate at Department of Electrical engineering, School of Engineering and Sciences, Tecnologico de Monterrey, Mexico, and as a postdoctoral fellow at Research Laboratories of Electronics (RLE) at Massachusetts Institute of Technology, Cambridge, USA. Currently, she is the director of Writing Lab at the Institute for the Future of Education within Tecnologico de Monterrey which focuses on educational research, faculty training, and enhancing the publication record of Tecnologico de Monterrey. She also holds the position of research professor at the School of Engineering and Sciences, Tecnologico de Monterrey, Mexico.

• Introduction to R programming and RStudio Integrated Development Environment (IDE).- Working with Data in R: Objects, Vectors, Factors, Packages and Libraries, and Data Visualization.- Test of Normality and Reliability of Data in R.- Choosing between Parametric and Non-Parametric Tests in Statistical Data Analysis.- Understanding Dependent and Independent Variables in Research Experiments and Hypothesis Testing.- Understanding the Different Types of Statistical Data Analysis and Methods.- Regression Analysis in R: Linear and Logistic Regression.- T-test Statistics in R: Independent samples, Paired sample, and One sample ttests.- Analysis of Variance (ANOVA) in R: One-way and Two-way ANOVA.- Chi-squared (X2) Statistical Test in R.- Mann Whitney U test and Kruskal Wallis H test Statistics in R.- Correlation Tests in R: Pearson cor, Kendall’s tau, and Spearman’s rho.- Wilcoxon Statistics in R: Signed-Rank test and Rank-Sum test.