2025
Statistics for Business I
Name: Statistics for Business I
Code: GES12667M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Management
Teaching languages: Portuguese
Languages of tutoring support: Portuguese, English
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
With this Curricular Unit, with eminently practical nature, it is intended that the students learn the basis of statistics to get a basic formation to manipulate files, analysis and graphic representations, using the SPSS
Competences to acquire:
? Capacity to team work inside an organization;
? Decision making capacity e solving problems connected with the statistics;
? Cap.to manage in good time;
? Capacity do draw, plan and conduct quality processes as well as confront the results with the observed facts;
? Cap. of critical spirit and models construction;
? Cap. of critic and self-critic in order to reach the continuous improvement;
? Cap. to integrated thinking;
? Specific competences concerning statistical analysis management and Information technologies;
? To handle, apply and implement methods, techniques and tools of statistical analysis
? Cap. to implement and use of ICT to support the management;
? Adequate and solid formation in qualitative methodologies;
Competences to acquire:
? Capacity to team work inside an organization;
? Decision making capacity e solving problems connected with the statistics;
? Cap.to manage in good time;
? Capacity do draw, plan and conduct quality processes as well as confront the results with the observed facts;
? Cap. of critical spirit and models construction;
? Cap. of critic and self-critic in order to reach the continuous improvement;
? Cap. to integrated thinking;
? Specific competences concerning statistical analysis management and Information technologies;
? To handle, apply and implement methods, techniques and tools of statistical analysis
? Cap. to implement and use of ICT to support the management;
? Adequate and solid formation in qualitative methodologies;
Contents
Module 1. Descriptive Statistics
1.1. Central measure location
1.2. Deviation measures (Variance, Standard error, Correlation coefficient its analysis as a measure of markets risk)
Module 2. Making a Database using the SPSS program
Module 3. Inference statistics
3.1 Estimation and properties of estimators
3.2 Confidence intervals
3.3. Tests of statistical hypothesis
Module 4. Regression analysis with seccional data
4.1 Hypothesis of OLS
4.2. Estimation of OLS
4.3. Properties of OLS estimators
4.4. Regression analysis with qualitative independent variables
4.5. Inference analysis in regression context
4.6. Empirical applications in management
1.1. Central measure location
1.2. Deviation measures (Variance, Standard error, Correlation coefficient its analysis as a measure of markets risk)
Module 2. Making a Database using the SPSS program
Module 3. Inference statistics
3.1 Estimation and properties of estimators
3.2 Confidence intervals
3.3. Tests of statistical hypothesis
Module 4. Regression analysis with seccional data
4.1 Hypothesis of OLS
4.2. Estimation of OLS
4.3. Properties of OLS estimators
4.4. Regression analysis with qualitative independent variables
4.5. Inference analysis in regression context
4.6. Empirical applications in management
Teaching Methods
Throughout the collective teaching sessions of the Course Unit, the syllabus contents will be presented in as much detail as possible, while encouraging dialogue with students regarding the importance and practical application of the knowledge acquired.
Given the highly practical and instrumental nature of the topics covered, an operational approach will be prioritised, based on the resolution of practical cases related to the day-to-day activities of organisations and individuals. A set of documents related to the subject matter of the Course Unit will also be analysed, and practical work using software tools, namely Jamovi and Excel, will be carried out. Asynchronous classes will be aimed at the development of assignments, while synchronous classes will be used for the exploration and application of the software tools.
The use of Artificial Intelligence is encouraged in accordance with Order No. 34/2026 ? Guidelines for the Use of Artificial Intelligence (AI) in the Teaching, Assessment and Learning Process at the University of Évora.
In order to promote closer contact and greater interaction with students, as well as to provide access to supporting materials related to the syllabus contents taught throughout the semester, the lecturer will use Moodle.
Given the highly practical and instrumental nature of the topics covered, an operational approach will be prioritised, based on the resolution of practical cases related to the day-to-day activities of organisations and individuals. A set of documents related to the subject matter of the Course Unit will also be analysed, and practical work using software tools, namely Jamovi and Excel, will be carried out. Asynchronous classes will be aimed at the development of assignments, while synchronous classes will be used for the exploration and application of the software tools.
The use of Artificial Intelligence is encouraged in accordance with Order No. 34/2026 ? Guidelines for the Use of Artificial Intelligence (AI) in the Teaching, Assessment and Learning Process at the University of Évora.
In order to promote closer contact and greater interaction with students, as well as to provide access to supporting materials related to the syllabus contents taught throughout the semester, the lecturer will use Moodle.
Assessment
At the University of Évora, there are two main assessment regimes: Continuous Assessment and Final Assessment.
According to the Academic Regulations of the University of Évora, under the Continuous Assessment regime?which consists of completing the various assessment components defined in the Course Unit Syllabus during the teaching period?the final grade is calculated based on the grade obtained in a practical assignment (individual or group) and the grade obtained in the examination (written test). The final grade is calculated as a weighted average, with 50% allocated to the practical assignment and 50% to the examination.
A minimum grade of 7 out of 20 is required in each assessment component.
Under the Final Assessment regime, if the student has not completed the practical assignment, only the examination grade will be considered. If the student has completed the assignment, the final grade will correspond to the higher of the examination grade and the weighted average of the examination and practical assignment grades.
If there are doubts regarding the assessment, students may be required to orally discuss and defend their assignments, in accordance with Order No. 216/2026, concerning the prevention of academic fraud, particularly in situations involving suspected improper use of Artificial Intelligence tools or other forms of academic misconduct.
According to the Academic Regulations of the University of Évora, under the Continuous Assessment regime?which consists of completing the various assessment components defined in the Course Unit Syllabus during the teaching period?the final grade is calculated based on the grade obtained in a practical assignment (individual or group) and the grade obtained in the examination (written test). The final grade is calculated as a weighted average, with 50% allocated to the practical assignment and 50% to the examination.
A minimum grade of 7 out of 20 is required in each assessment component.
Under the Final Assessment regime, if the student has not completed the practical assignment, only the examination grade will be considered. If the student has completed the assignment, the final grade will correspond to the higher of the examination grade and the weighted average of the examination and practical assignment grades.
If there are doubts regarding the assessment, students may be required to orally discuss and defend their assignments, in accordance with Order No. 216/2026, concerning the prevention of academic fraud, particularly in situations involving suspected improper use of Artificial Intelligence tools or other forms of academic misconduct.
Teaching Staff
- Andreia Teixeira Marques Dionísio Basílio [responsible]
Accreditations and Certifications
