DescriptionR is a powerful and flexible statistical language and environment for statistical analysis and graphics. It is free, open source, and runs under most operating systems. R becomes an increasing important and widely used tool for applied statistical modeling. There are thousands of contributed packages available in R to perform a great variety of statistical and graphical procedures. New statistical methods are often available first in R. This short course will introduce KAUST students to the basics of using R for statistical programming, computation, graphics, and modeling. Some important statistical concepts, such as exploratory data analysis, hypothesis testing, regression analysis, cross-validation, permutation test, and bootstrap, will also be discussed while working with R. There are no specific prerequisites but some knowledge of statistics would be helpful. A basic knowledge of matrix algebra, computers, and the internet will be assumed.
Dr. Jianhua Huang is a professor in Department of Statistics at Texas A&M University and the co-leader of Core II (Deterministic and Statistical Inverse Problems) in the KAUST GRP center at Texas A&M. He is an active researcher in the areas of computational statistics, statistical machine learning, nonparametric and semiparametric methods, spatial statistics, survival analysis, functional and longitudinal data analysis, and statistics applications in business and engineering. Dr. Huang has taught various statistics courses including applied statistics, mathematical statistics, multivariate analysis, nonparametric statistical methods, regression analysis, statistical finance, statistical machine learning, and time series analysis.
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