Statistics Applied to Business Sciences

Recommended prior knowledge corresponds to the fundamental Statistics content included in the Mathematics curricula for the different levels of compulsory education in Portugal (http://www.dge.mec.pt/), as well as the knowledge acquired in the courses Quantitative Methods Applied to Business Sciences and Management Information Systems, namely:

• PK1. Basic knowledge of descriptive statistics.
• PK2. Ability to interpret statistical tables, graphs, and indicators.
• PK3. Basic computer skills and proficiency in the use of common software applications.
• PK4. Fundamental knowledge of quantitative methods applied to business sciences and management information systems.

• TM1. Theoretical-Practical Classes: In accordance with the curriculum, classes are theoretical-practical in nature and are planned and designed to promote students’ active engagement throughout different stages or the entirety of each class.
• TM2. Interactive Lectures: The theoretical component, including the introduction of concepts, methods, and fundamental results, will be delivered through lectures complemented by strategies that promote active student participation (interactive lectures). These strategies include questions posed by both the instructor and the students, either orally or through digital platforms, as well as debates and small-group discussions on the topics addressed.
• TM3. Guided Problem Solving and Practical Activities: The practical component is designed to fully develop the intended competencies through guided demonstrations of procedures and/or problem-solving activities under the instructor’s supervision. Independent work and small-group collaboration will be encouraged. A strong integration between theory and practice will be emphasized, with particular focus, whenever possible, on the visualization and analysis of real-world situations.
• TM4. Independent Learning: The development of the competencies defined for this course requires active and regular participation in class, together with the independent completion of the activities proposed before, during, and after the face-to-face sessions.
• TM5. Learning Resources: All supporting materials will be made available through the InforEstudante|Nonio platform. Additional digital platforms may also be used whenever appropriate to support teaching activities and promote interaction with students.

Statistical data analysis plays a fundamental role in a wide range of business contexts by supporting the description and diagnosis of phenomena of interest, as well as data-driven decision making. Within this context, the learning outcomes and competencies to be developed in this course focus on the application of statistical methods and computational tools for data analysis and interpretation, promoting informed decision making.

Learning outcomes:

• LO1. Portray data sets through univariate and bivariate descriptive statistical analysis.
• LO2. Perform a simple regression analysis to explain a phenomenon of interest.
• LO3. Extract relevant information about a phenomenon by conducting descriptive analysis of the associated dataset.
• LO4. Debate the veracity of claims based on statistical analysis of the associated dataset.
• LO5. Formulate and conduct small-scale statistical investigations to support data-driven decision making.

Competencies to be developed:

• SC1. Situation/Problem Identification: Recognize situations that may benefit from a descriptive, univariate, bivariate, or regression statistical study, or inferential statistical analysis.
• SC2. Technique Selection: Identify, among a set of basic statistical techniques, those suitable for processing a certain set of data.
• SC3. Data Organization: Organize a set of data in tables and graphs for frequency distribution analysis.
• SC4. Statistical Summarization: Select the most appropriate measure or measures to summarize a set of data, based on a specific purpose.
• SC5. Graphical Interpretation: Comment on the relevant information contained in a graph.
• SC6. Graphical Communication: Illustrate a statement with a suitable graphic.
• SC7. Research Planning: Describe the stages of a statistical investigation.
• SC8. Research Formulation: Formulate a research question relevant to a proposed problem.
• SC9. Hypothesis Testing: Apply a hypothesis test to answer the research question.
• SC10. Interval Estimation: Construct a confidence interval suitable for the investigation.
• SC11. Results Interpretation: Results Interpretation: Draw statistical conclusions from the conducted investigation.
• SC12. Communication of Results: Translate statistical conclusions into research results.
• SC13. Software Utilization: Use software to support obtaining the results.

• P1. Descriptive statistics
  1.1. Basic concepts
  1.2. Data representation
  1.3. Statistical measures
• P2. Simple linear regression
  2.1. Correlation
  2.2. Simple linear regression model
  2.3. Least squares regression line
• P3. Statistical inference
  3.1. The role of probability distributions
  3.2. Central limit theorem.
  3.3. Parametric estimation: point and interval estimation
  3.4. Parametric hypothesis testing for one population
  3.5. Introduction to statistical investigations

Within the curriculum of the Bachelor's Degree in Executive Secretariat and Administration, the course Applied Statistics for Business Sciences is allocated 6 ECTS credits, corresponding to a total student workload of 160 hours, of which 30 hours are contact hours. In accordance with the Academic Regulations for First-Cycle Degree Programs of the Polytechnic University of Coimbra (IPC), students may complete the course under one of the following assessment schemes:

A) Continuous Assessment

This assessment scheme consists of two written tests administered during the teaching period.

AM1. Written Test 1 (50%)

• Weight: 10 points
• Minimum required grade: 3.5 points
• Mandatory
• Closed-book

AM2. Written Test 2 (50%)

• Weight: 10 points
• Minimum required grade: 3.5 points
• Mandatory
• Closed-book

Final grade formula:

FG = AM1 + AM2

The final grade is calculated provided that the student obtains a minimum grade of 3.5 points (minimum required grade) in both tests. Failure to meet this requirement results in failure under this assessment scheme.

Notes:

• Class attendance is not mandatory for eligibility under this assessment scheme; however, regular attendance is strongly recommended.
• The tests may be completed on a computer using the software employed during the classes and one or more platforms requiring institutional authentication. Students intending to take the tests must follow all instructions published on the InforEstudante|Nonio platform (e.g., ensuring that they know their passwords and are able to access the required platforms).
• Each test covers the content taught up to the class immediately preceding the respective test.
• If a student is absent from a test, regardless of the reason, no make-up test will be scheduled, and a grade of 0 (zero) will be assigned.

B) Final Examination

AM3. Written Examination (100%)

• Weight: 20 points
• Mandatory
• Closed-book
• Administered on the date established in the official examination schedule.
• The examination may be completed on a computer using the software employed during the classes and one or more platforms requiring institutional authentication. Students intending to take the examination must follow all instructions published on the InforEstudante|Nonio platform (e.g., ensuring that they know their passwords and are able to access the required platforms).

Final grade formula:

FG = AM3

The final grade corresponds to the grade obtained on the examination.

General Assessment Provisions:

• Passing requirement: Under either assessment scheme, students pass the course by obtaining a final grade, rounded to the nearest whole number, of at least 10 points.
• Guidelines on the Use of Generative Artificial Intelligence: The use of Generative Artificial Intelligence tools is not permitted during any assessment component of the course.
• Academic Integrity: In the event of a well-founded suspicion of academic misconduct identified during the grading of an assessment, the student will be required to take an oral examination to verify the knowledge and competencies demonstrated. This verification will be conducted by a panel consisting of the course instructor and two additional faculty members from the same academic area. If the student fails to demonstrate the knowledge and competencies evidenced in the written assessment, the assessment will be declared null and void.
• Grade Improvement: Grade improvement is governed by the Academic Regulations for First-Cycle Degree Programs of the IPC.
• Students with Worker-Student Status: Assessment of students holding Worker-Student status is governed by the Academic Regulations for First-Cycle Degree Programs of the IPC.

NAO