1) Goals : This course introduces the basic concepts of inferential statistics. The key goal is to lay out the foundations for the advanced statistics/econometrics courses.
2) Course overview :
Chapter 1 : Estimation
- Basic concepts : parameter, estimator, estimate, bias, variance, mean squared error (MSE), confidence interval
- Examples : empirical mean/variance especially in the Gaussian case, maximum likelihood estimator (MLE), least squares estimator
Chapitre 2 : Tests
- Basic concepts : null/alternative hypothesis, test statistic, type I and type II errors
- Examples : tests in the Gaussian case (about the value of a mean, about the equality of means), significance of a regression coefficient