This group of courses are developed at an undergraduate level in statistics. Only basic level knowledge of statistics and R is required at the beginning of the course. The courses aim to teach the students basic topics in inference (hypothesis testing), modeling (regression) and basic ideas in exploratory data analysis and computational statistics. The courses in inference and regression are also available online (http://sia.webpopix.org/). Slides for the course in regression and inference (I) are available for offline usage.

This course presents the basic concepts of linear regression. This is an online course which was developed by Marc Lavielle within his initiative statistics in action with R (http://sia.webpopix.org/). All R code for the examples presented in the course is avilable online. Slides which cover most of the materails presented in the course are avilable.

  • Basic concept in statistical inference using R (I): single comparison(See Basic Inference(I)).

This course presents the basic concepts of hypothesis testing for single comparison. This is an online course which was developed by Marc Lavielle within his initiative statistics in action with R (http://sia.webpopix.org/). R code for all examples illustrated in the course is available online. In addition, slides and YouTube tutorials are available in the repositories of the course.

  • Basic concept in statistical inference using R (II): multiple comparisons (see Basic Inference (II))

This course presents the basic concepts of hypothesis testing multiple comparisons. Itis an online course which was developed by Marc Lavielle within his initiative statistics in action with R (http://sia.webpopix.org/). R code for all examples illustrated in the course is available online. Slides will not be avilable in September 2017

  • Basic concepts in exploratory data analysis (EDA) and computational statistics (see EDA)

This course presents the basic concepts in EDA and computational statistics. Basic Exploratory data analysis in R (Location, Spread, Resistance and Robustness). The course covers selected chapters from the book “Understanding robust and exploratory data analysis” by David C. Hoaglin, Frederick Mosteller and John W. Tukey (Editors).In addtion, the course covers topics related to computational statistics (Basic bootstrap, Likelihood and basic optimization). For the basic bootstrap, the course covers chapters 4-5-6 from the book “An Introduction to the Bootstrap” by B. Efron and R. Tibshirani.