Base Knowledge
.
Teaching Methodologies
The teaching method is initially expositive and later, during the problem solving phase, collaborative.
Learning Results
Learn the fundamental concepts of:
Probabilities;
Statistics;
Data Analysis using appropriate software.
Relate the language / concepts learned with real problems of everyday life.
In all learning objectives there is another great goal: to relate with Electromechanical Engineering the concepts and techniques acquired.
Program
1. Probabilities: Introduction, Experience random space results, events, Definition of probability, conditional probability, independent events, the total probability theorem, Bayes Theorem.
2. Random Variables: Random variables dimensional discrete and continuous: probability / density function, distribution function, independence of random variables, random continuous parameters of random variables, Tchebychev Inequalities, Markov and Bienaymé-Tchebychev, Covariance and linear correlation coefficient.
3. Some Special Discrete and Continuous Distributions: Bernoulli, Binomial, Hyper geometric, Poisson, Uniform, Normal, Exponential. Central limit theorem. Approximate Distributions. Chi-square, Student’s t, Sampling distributions.
4. Statistical Inference: Point estimation, interval estimation.
5. Parametric hypothesis tests.
Internship(s)
NAO