Statistics for Enterprise Data Analysis


This curricular unit aims at supplying to the students the theoretical and practical knowledge about methodologies on summarizing data, and parametric statistical inference. Students will explore the core principles of statistics, from both the conceptual and applied perspectives. The students will acquire competences related to descriptive statistics, random variables, sampling and confidence intervals, hypothesis testing, regression model and time series linear models. The students will clearly understand the conditions of applicability of each procedure. The concepts and principles will be applied and discussed using the environment, functions, and visualizations of R using real-world concepts applicable to many industries, including medical, business, sports, insurance, etc.

General characterization





Responsible teacher

Jorge Morais Mendes


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


Not applicable


  • Pardue, I. (2012) Applied Regression Modeling, 2nd edition, Wiley.
  • Lock, Robin, H., Lock, P. F., Morgan, K.L., Lock, E.F.; Lock, D.F. (2017) Statistics: unlocking the power of data. 3rd edition, Wiley.

Teaching method

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications using Microsoft Excel, questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results.

Evaluation method

1st round: the highest grade among the following: (1) Final exam (60%) or (2) Project (50%) + Final exam (50%).

2nd call: the highest grade among the following: (1) Final exam (60%) or (2) Project (50%) + Final exam (50%).

Subject matter

LU1. Collecting data\\ \midrule
LU2. Describing data
LU3. Confidence intervals
LU4. Hypothesis testing
LU5. Normal distributions
LU6 Inference

LU7. Simple linear regression
LU8. Multiple linear regression


Programs where the course is taught: