AIMS AND CONTENT

AIMS AND LEARNING OUTCOMES

The learning objectives that will be evaluated for the purpose of passing the final exam are summarized in the following scheme:

Knowledge and understanding: Knowledge of the main tools for the synthesis and presentation of data, through the acquisition of the main techniques of descriptive statistics; knowledge of probabilistic techniques for the analysis of simple random phenomena; acquisition of basic statistical inference tools for estimation and hypothesis testing.

Ability to apply knowledge and understanding: Ability to use the appropriate techniques based on the type of data under analysis; be able to carry out basic descriptive analyzes for univariate and bivariate phenomena using the main summary indices; be able to carry out simple computations in situations of uncertainty; know how to apply the main statistical inference techniques; know how to read statistical analyses carried out with the methodologies presented in the course.

Making judgements: Be able to understand and comment on the results obtained from statistical analyses in practical examples based on the context of the application, thus being able to use the results in decision-making processes.

Communication skills: Acquire the basics of technical statistical language to communicate clearly and without ambiguity with both statisticians and non-statisticians.

Learning skills: Be able to correctly read the results of statistical analyses, also in contexts of greater complexity than those presented in the course.

SYLLABUS/CONTENT

Part I: Descriptive statistics

1. Introduction to statistics. Descriptive and inferential statistics. Populations and samples.
2. Distributions, frequencies, and cumulative frequencies. Graphs to describe categorical variables, time series, and quantitative variables. Graphs and tables to describe relationships between variables.
3. Location measures: mean, median, mode, percentiles.
4. Variability measures. Box-plot. Measures of  concentration.
5. Measures of relationships between variables.

Part II: Probability

1. Random experiments, outcomes, events.
2. The probability function and its axioms. Rules of probability.
3. Conditional probability and independence. Bivariate probabilities.
4. Bayes’ theorem.
5. Discrete random variables and their properties.
6. Bernoulli and Binomial distributions.
7. Continuous random variables and their properties.
8. Normal distribution.

Part III: Inference

1. Sampling and sampling distributions.
2. Distribution of the sample mean. The central limit theorem. Distribution of the sample proportion.
3. Point estimation. Estimators and their properties.
4. Confidence intervals. Confidence intervals for the mean and for the proportions.
5. Confidence interval for a proportion
6. Theory of statistical hypothesis testing. Test for a mean, test for a proportion.
7. Comparison of means.

RECOMMENDED READING/BIBLIOGRAPHY

Newbold, Carlson, Thorne, Statistica. Nona edizione. Pearson (2021). Foreign students can refer to the original version of this book. For a topic not covered by the textbook, documentation will be provided in Aulaweb by the teacher.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Classes will start in september

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam consists of a written/practical test with multiple-choice questions and some exercises to be solved using Excel.

ASSESSMENT METHODS

The multiple-choice questions are designed to assess the knowledge and understanding of the topics covered in class, while the exercises are intended to evaluate the student's ability to apply the methods learned.