MAA210 provides with an introduction to the beasic concepts of probability and statistics. The course covers the following topics
- Discrete probability spaces: independence and conditional probability
- Discrete random variables: independence, joint distribution and marginal distribution.
- Expectation and variance of a discrete random variable: applications to binomial random variables, Poisson random variables and geometric random variables.
- Basic inequalities: Jensen, Markov and Chebyshev.
- Absolutely continuous random variables: Gaussian random variables, uniform random variables and exponential random variables
- Cumulative distribution function
- Expectation of an absolutely continuous random variable
- Quadratic convergence of random variables and law of large numbers for square integrable random variables
- Introduction to descriptive Statistics: of one and two-dimensional descriptive statistics (correlation and LS regression line)
- Random variables: variance, moment generating function, characteristic functions
- Convergence of a sequence of random variables and central limit theorem
- Introduction to parametric estimation: Estimation of the expectation, Maximum likelihood estimation
- Confidence interval and introduction to statistical tests: Confidence intervals, parametric tests
MAA210 provides with an introduction to the beasic concepts of probability and statistics. The course covers the following topics
- Discrete probability spaces: independence and conditional probability
- Discrete random variables: independence, joint distribution and marginal distribution.
- Expectation and variance of a discrete random variable: applications to binomial random variables, Poisson random variables and geometric random variables.
- Basic inequalities: Jensen, Markov and Chebyshev.
- Absolutely continuous random variables: Gaussian random variables, uniform random variables and exponential random variables
- Cumulative distribution function
- Expectation of an absolutely continuous random variable
- Quadratic convergence of random variables and law of large numbers for square integrable random variables
- Introduction to descriptive Statistics: of one and two-dimensional descriptive statistics (correlation and LS regression line)
- Random variables: variance, moment generating function, characteristic functions
- Convergence of a sequence of random variables and central limit theorem
- Introduction to parametric estimation: Estimation of the expectation, Maximum likelihood estimation
- Confidence interval and introduction to statistical tests: Confidence intervals, parametric tests
- Teaching coordinator: Aurillard Antoine
- Teaching coordinator: Bardet Jean Marc
- Teaching coordinator: Conforti Giovanni
- Teaching coordinator: Eichinger Katharina
- Teaching coordinator: Kubasch Madeleine
- Teaching coordinator: Marivain Maxime
- Teaching coordinator: Van Biesbroeck Antoine