Programma

Part I: Introduction to R and usage of Rstudio
1. R Environment
2. Rstudio: the compiler and its features
3. packages, libraries and functions in R
Part II R objects
1. Vectors, matrices and dataframe, list, class S4 objects
2. plotting the results: boxplots, histograms, graphical representation of
contingency tables
3. User defined functions
Part III: Statistical Models in R
1. Simple Linear Regression Model
2. Multiple Linear Regression Model
3. Clustering Models in R
4. Fundamentals of Machine Learning
Part III: Other Information
1. Final Exam: Hands-on exercise based on the commands learned in
class. The course is graded on a pass/fail basis
2. Learning Materials:
ˆ Script and slides provided by the instructor
ˆ pdf book available at https://www.econometrics-with-r.org/ITER.pdf

Programma

Part I: Introduction to R and usage of Rstudio
1. R Environment
2. Rstudio: the compiler and its features
3. packages, libraries and functions in R
Part II R objects
1. Vectors, matrices and dataframe, list, class S4 objects
2. plotting the results: boxplots, histograms, graphical representation of
contingency tables
3. User defined functions
Part III: Statistical Models in R
1. Simple Linear Regression Model
2. Multiple Linear Regression Model
3. Clustering Models in R
4. Fundamentals of Machine Learning
Part III: Other Information
1. Final Exam: Hands-on exercise based on the commands learned in
class. The course is graded on a pass/fail basis
2. Learning Materials:
ˆ Script and slides provided by the instructor
ˆ pdf book available at https://www.econometrics-with-r.org/ITER.pdf