This course provides a foundation in probability and statistics and introduces basic regression analysis. In probability theory, we cover the basic concepts from probability spaces over random variables up to convergence. This theoretical equipment is needed for inductive statistics and regression analysis that aims at drawing conclusions from samples about causes and regularities underlying the population.
 

• Familiarity with the basic concepts in probability, statistics, and regression analysis : awareness of theoretical knowledge and first experience in handling and describing data, estimation, and testing using the software R.
• Capacity to compile basic descriptives, and to formulate and test hypothesis about the data generation process, including basic regression analysis

 

• Cameron, A.C. and Trivedi, P.K. (2005), “Microeconometrics: Methods and Applications”, Cambridge University Press, New York.
• Dekking, F.M., Kraaikamp, C., Lopuhaä, H.P., Meester, L.E. (2005) “A Modern Introduction to Probability and Statistics: Understanding Why and How”, Springer-Verlag London Limited.
• Li, Q. and Racine, J.S. (2007), “Nonparametric Econometrics: Theory and Practice”, Princeton University Press.
• Mittelhammer, R.C. (2013) “Mathematical Statistics for Economics and Business, Second Edition”, Springer Science+Business Media New York.
• Schumacker, R., Tomek, S. (2013) “Understanding Statistics Using R”, Springer Science+Business Media New York.