DAES04B - ANALISI INFERENZIALE DEI DATI

The course pursues the general objective of the study program, which is to train professionals skilled in the management and inferential analysis of data in all work contexts.
The course aims to provide students with the fundamental concepts of statistical inference, starting from the notions of population, sample, and parameter. The main sample statistics will be introduced, and statistical inference techniques such as point estimation, interval estimation, and hypothesis testing will be studied to estimate population parameters. Additionally, the properties of estimators will be analyzed. The concepts of likelihood and sufficiency will be presented, along with the distinction between parametric and non-parametric tests, with particular attention given to the Chi-Square test. Finally, inference on conditioned means will be covered.
Upon completion of the course, students should possess knowledge and skills related to statistical terminology, the ability to estimate population parameters (such as mean and variance) using point estimation techniques and confidence intervals. Moreover, students will be able to formulate statistical hypotheses and conduct tests to verify them, correctly interpreting the results.
The teachings of the course are integrated with the educational objectives of the entire study program, which aims to provide the necessary skills to develop analyses and research in statistical disciplines applicable across various economic and societal contexts. Specifically, students will acquire the knowledge needed to carry out data analysis, design and conduct surveys, with descriptive, interpretive, and decision-making objectives.
To this end, the course aims to transmit the following competencies and knowledge:
KNOWLEDGE AND UNDERSTANDING
Understand the concepts of population, sample, and parameter
Understand the logic behind constructing sampling distributions and the implications for statistical inference
Understand the philosophy and scientific principles of hypothesis testing and the interpretation of results.
The course seeks to impart the following specific competencies and knowledge:
KNOWLEDGE AND UNDERSTANDING (applied)
Formulate tests for homogeneity, independence, and goodness-of-fit, interpreting the results.
Apply estimation methods to sample data to make inferences about population parameters.
INDEPENDENT JUDGMENT
Be able to identify, among the various tools and approaches available, those that allow for adequate quantitative analysis in relation to the objectives.
Decide independently which tools to use and which analyses to perform in order to infer population parameters of interest based on the available sample data.
Understand, interpret, and provide a critical evaluation of the results of inferential data analysis.
COMMUNICATION SKILLS
Express oral analytical and synthetic considerations on the fundamental aspects of the discipline and make interdisciplinary connections.
Use statistical language appropriately and pertinently.
Communicate statistical analysis results and the underlying logical reasoning in both written and oral form.
LEARNING ABILITY
Conduct individual and group research on specific aspects of the discipline.