Data Analysis and Statistics

The classes are designed, according to the curriculum plan, to be both theoretical and practical. They are planned and prepared
considering active learning activities, to actively engage all students at various moments or throughout the entire class.
In the theoretical part of the lesson, the expository method will be frequently used to introduce concepts, fundamental results, and methods,
interspersed with tasks that encourage active participation by all students. These tasks include posing questions to and by students, orally
and/or on a platform, as well as proposing debates/discussions in small groups on certain exposed aspects/topics.
The practical part will be designed to comprehensively develop the listed skills. This will be achieved through commented exemplification of
procedures and/or problem-solving under the guidance/tutoring of the teacher. Autonomous work or work in small groups will be
encouraged. There will be a strong interaction between theory and practice, with a central focus on visualizing and dealing with actual
scenarios.

Statistical data analysis is relevant in many business contexts, to describe, explore, diagnose and help understand real phenomena,
providing support for decision-making. Statistics has a large number of applications in various areas of knowledge and professional
segments, particularly in marketing.
The learning objectives are:
1. identify contexts that can benefit from a data-based study
2. collect and describe sets of data to be analyzed
3. generate insights into a phenomenon through exploratory analysis of the associated data set
4. solve problems involving scenarios of uncertainty that can be described in probabilistic terms
5. identify and apply statistical inference methods such as estimation and hypothesis testing that are suitable for solving real marketing
problems
6. use software to support the implementation of statistical techniques

Introduction
1.1. Framework: statistical thinking
1.2. Data types
2. Fundamental data analysis
2.1. Univariate descriptive analysis
2.2. Bivariate descriptive analysis
3. Probabilities and theoretical distributions
3.1. Fundamental concepts and theorems
3.2. Discrete and continuous random variables
3.3. Discrete and continuous theoretical distributions
4. Statistical inference
4.1. Introduction
4.2. Confidence intervals for different population parameters
4.3. Parametric hypothesis tests for different population parameters
4.4. Nonparametric tests

NAO