STATISTICS 2: PRACTICE

The course is one of the core educational activities of the Business Administration and Management degree program that allows students to acquire the knowledge and understating of the main statistical concepts used in management disciplines.
The aim of the course is to provide the basic knowledge of descriptive statistics, probability theory and inferential statistics to develop skills that allow them to structure, process and analyze data in order to describe and make inference in problems characterized by uncertainty such as in economics, finance, quality control and marketing.

At the end of the course, students will be expected to have acquired the basic knowledge of statistics to develop descriptive and inferential analyses of business phenomena through tools and statistical methods suitable for the analysis, as for example graphics and numerical synthesis, punctual or interval estimates, hypothesis tests. In particular, students should:
1. Knowledge and understanding
- know the terminology and basic concepts of descriptive and inferential statistics for business phenomena;
- know the basic notion of probability theory;
- understand the strengths and limitations of the statistical approaches used to analyze business phenomena.
2. Ability to apply knowledge and understanding
- know how to determine the best statistical methods for the analysis of real phenomena;
- understand the main aspects of the descriptive and inferential statistical analyses;
- know how to apply the main probabilistic models to business.
3. Making judgements
- be able to critically assess under which circumstances the analyses are reliable;
- be able to assess the goodness of the achieved results.
4. (Lifelong) learning skills
- be able to critically consult reference books and online material in the fields of statistics and analysis of data for business phenomena.

The formal prerequisite is the successful completion of the first-year course in Mathematics.

Below the full content of the 12 CFU course:
1. Data analysis.
2. Probability.
3. Random variables and probability distributions.
4. Sample statistics.
5. Estimation.
6. Testing statistical hypotheses.
7. Linear regression
To support the theoretical knowledges acquired during the course, each topic will be presented by using business examples.

Mandatory texts:
Anderson, D. R., Sweeney, D., Williams, T. (2010). Statistica per le analisi economico-aziendali, Apogeo.

Additional readings
Ross S. M. (2008). Introduzione alla Statistica, Apogeo.
Levine D. M.; Krehbiel T. C.; Berenson M. L. (2010 o 2014). Statistica, Pearson.
Other reading material suggested by the teacher during the course.

The exam aims to verify that the student has acquired the concepts presented during the lectures and is familiar with the tools proposed for data analysis and inferential procedures. Specifically, the exam consists of a written test of approximately 1 hour and 15 minutes, which includes quizzes and exercises sufficiently adequate in number to verify the understanding of the program covered, each with its own assigned score. Specifically, the test will be composed of 6 quizzes related to the program of the first part and 6 quizzes related to the program of the second part, each with a score of 1 point. Additionally, there will be one structured exercise related to the program of the first part and one structured exercise related to the program of the second part, each with a maximum score of 10 points. The total maximum score available is 32 points.
Quizzes and exercises similar to those proposed in the final exam will be conducted both during theoretical lessons and practical exercises.
The exam will be open-book (paper material only), the use of a pocket calculator is allowed (no PC or tablet), as well as distribution tables and any material provided in advance by the instructor.

The course consists of fifteen lessons for the first part and fifteen lessons for the second part to introduce the basic knowledge of descriptive and inferential statistics. Moreover the basic notion of probability theory will be presented. Methods will be discussed and illustrated through business examples.
Ten exercise lessons are provided to study in deep the topics of the course.

Students should register in the related course web page of the university e-learning platform moodle.unive.it

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