Statistics: Inference

The first part of the course introduces the main techniques for estimating the parameters of a statistical model. It covers estimators based on the method of moments and maximum likelihood estimation. The second part of the course addresses hypothesis testing for model parameters and the construction of confidence intervals. The course is structured as follows:

• Introduction to Statistical Inference – Sample selection, population characteristics, parametric models.
• Sampling – Basic principles.
• Estimation Theory – Definition and properties of an estimator, parametric estimation methods.
• Hypothesis Testing – Problem formulation: null and alternative hypotheses, type I and type II errors, p-value. Main tests: test on the mean, test on the difference between two means, Student’s t-test.

The course introduces the fundamental concepts of statistical inference, namely parameter estimation and hypothesis testing.

In presence

The course is taught in person.

Online computer-based quiz.