1. Introduction to probability and statistics (E1)
Week 1———–1.1 Probability rules.
Week 2———–1.2 Random variables and Probability Models (PDF and CDF).
Week 3———–1.3 Random sampling and expected values.
Week 4———–1.4 Sampling Distribution and the CLT.
2. Inference (E2)
Week 5-6———2.1 Descriptive statistics.
Weeks 7-9——-2.2 Inferential statistics.
Weeks 10——–2.3 Bayesian inference.
3. Computational experimental designs (E3)
Week 11———3.1 Experimental design.
Weeks 12-14—3.2 Hypothesis tests for algorithm analysis.
Week 15-16—–3.3 Algorithm analysis project
The components of the course grade for each evaluation are:
Progress: it contains official syllabus, grades and attendance.
Notes downloading: go to each topic of the English syllabus.
Labs submission: see instructions.
Exams schedule on Fridays:
E1-W5, E2-W11, E3-W14.
Labs schedule on Mondays:
Walpole, Ronald E., Raymond H. Myers, Sharon L. Myers, and Keying Ye (1993). Probability and statistics for engineers and scientists. Macmillan.
SticiGui online book: http://www.stat.berkeley.edu/~stark/SticiGui/index.htm
Montgomery, D. C., & Runger, G. C. (2010). Applied statistics and probability for engineers. John Wiley & Sons.
Freedman, Pisani, and Purves (2007). Statistics (4th edition). Springer.
John Rice (2006). Mathematical Statistics and Data Analysis. Cengage.
Seefeld, Kim, and Ernst Linder (2007). Statistics Using R with Biological Examples. University of New Hampshire.
Teetor, Paul (2011). R cookbook. O’Reilly Media.
Dalgaard, Peter (2008). Introductory statistics with R. Springer.
R Lenguaje (scientific software for statistics): http://www.r-project.org/
JRI (run R inside Java): http://rforge.net/JRI/
R Commander: http://www.rcommander.com/