Statistics Applied to Business Sciences

 Math A or Math B

 Theoretical-practical classes, as stipulated in the curriculum.

• Theoretical component: exposition of concepts and resolution of illustrative examples.
• Practical component: resolution of illustrative and complementary practical exercises of the material exposed in theoretical classes, under the guidance of the teacher, but encouraging autonomous resolution. Sometimes using statistical software.

Statistical data analysis is relevant in numerous business contexts for the description and diagnosis of phenomena of interest, as well as for decision support. The goals and skills of the UC of Statistics Applied to Business Sciences focus on developing the statistical literacy needed to recognise and harness this potential.

 General objectives:

• Explore data using numerical and graphical methods, to identify its most relevant aspects, with application in the area of Management and Economics;
• Understand and apply probabilistic models that constitute the bases of statistical inference and decision making;
• Use statistical software as a tool to support data analysis and decision-making.

Specific objectives:

• Apply techniques of descriptive statistical analysis (exploratory) to characterise samples and communicate results;
• Analyse and interpret relationship between two variables;
• Apply inferential statistical techniques to draw conclusions about populations based on sample data.

Generic skills:

• Analyse and synthesise information with accuracy and appropriate justification;
• Communicate statistical results clearly, both orally and in writing;
• Use statistical software to obtain results, analyse them critically, and support their interpretation;
• Apply statistical thinking to solve problems in real-world contexts;
• Use critical thinking in data analysis and in evaluating conclusions and evidence based decisions.

Specific skills:

• Develop logical, deductive and critical reasoning in the analysis and resolution of statistical problems applied to real-world contexts;
• Combine intuition with statistical knowledge to formulate and test hypotheses;
• Interpret graphical representations and draw relevant conclusions;
• Summarize, analyse, and interpret data to support decision-making.

  1. Descriptive statistics:

• Basic concepts; data representation; statistical measures.

  2. Simple linear regression:

• Correlation; simple linear regression model; straight least squares; prediction.

  3. Probability distributions:

• Basic concepts of probability theory;
• Random variables;
• Parameters of distributions;
• Discrete probability distributions;
• Continuous probability distribution;
• Central Limit Theorem.

  4. Statistical inference:

• Parametric estimation: point estimation (estimation methods and estimator properties), interval estimation of the population mean (confidence intervals construction);
• hypothesis testing: fundamental notions, test architecture, parametric tests (a population).

NAO