Statistical Methods

6 ECTS / Semester / Portuguese

Objectives and competences

Objectives and competences

Transfer basic knowledge of statistics, including descriptive and statistical inference. To present the main probability distributions and their applicability. Promote autonomy, transferring knowledge to help them to be critical of that knowledge. Promote a scientific and critical thinking of results, essential in decision process.

Results expected:

Understand the concept of mathematical probability.
Understand the difference between random and systematic errors.
Describe and characterize the random variable with expectation value, variance and distribution functions.
To be able to analyze and explain large amounts of data by relevant descriptive statistics.
Compute estimators for the most important parameters (e.g. mean, variance).
Adjust the strategy of statistical analysis to the data type and its limitations.
Realize the regression and the model's validity and importance of estimation of confidence intervals.


Teaching Methodologies

The course is divided into theoretical and practical classes (TP). In the TP class theory will be presented with a large number of examples, and thereafter, students solve problems. Where appropriate cases will be selected appropriate to the engineering area in order to consolidate and apply the knowledge acquired

Parallel to the presentation of the fundamental theoretical concepts for understanding the materials will be used wherever possible, practical examples to illustrate these same concepts.

The adoption of a system of continuous assessment through tests/assignments allows students to progressively consolidate the materials and teachers to follow the students' learning. The realization of a final exam is designed to assess the capacity of integration of the different matters by students



  1. Basic concepts of statistics: Definition of statistics; Population and sample; Phases of the method of statistical analysis; Parameters, statistics and their symbols; Types of statistical variable; Comparison of different scales
  2. Descriptive statistics; frequency table; Characterization of samples; qualitative and quantitative data; Different chart types: adv- and disadvantages; Measures of central tendency, dispersion and no central tendency
  3. Introduction to Probability distributions; Normal; reduced normal distribution; sampling distribution; Central Limit Theorem; Binomial; approximation of binomial to normal Distribution; Poisson.
  4. Inference on Quantitative data (parametric methods): General Principles of Statistical Inference: Parameter Estimation; Hypothesis testing; T-means test: for 1 or 2 independent samples; 2-paired samples; Analysis of variance with 1-factor and 2-factor.
  5. Inference to qualitative data (non-parametric methods); Test for proportions.
  6. Linear Regression


Invited Assistant Professor
PhD in Biotechnology in 2006, from the Portuguese Catholic University. Consultant in Food Engineering, he is responsible for coordinating R & DT projects…