Statistical Methods

Theoretical lectures, and problem solving in practical lectures with teacher’s guidance.

Students acquire fundamental knowledge in Probability Theory and Statistical Inference and develop their problem solving and critical reasoning skills, using a more suitable language, models and techniques.

1.Probabilities:Random experiment,sample space,events.Probability measure.Conditional probability,Independence.Bayes’ theorem.
2.Discrete random variables and probability distributions:Probability mass and cumulative distribution functions,expected value,variance and standard deviation. Bernoulli,Binomial,Hypergeometric,Poisson. Two discrete random variables: joint, marginal and conditional distributions, independence, covariance, correlation.
3.Continuous random variables and probability distributions:Probability density and cumulative distribution functions, expected value, variance and standard deviation. Uniform, Exponential, Normal, t- Student, Chi-Square.
4.Random sampling and sampling distributions:Random sample.Statistics.Sampling distributions of the mean and variance.
5.Estimation:Fundamental concepts in point and interval estimation. Confidence intervals for the mean and variance.
6.Test of Hypothesis:Fundamental concepts. Test of hypothesis for the mean and variance.

NAO