Applied Statistics

Code:

M2041

Acronym:

M2041

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2025/2026 - 2S

Active?	Yes
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Bachelor in Bioinformatics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:BIOINF	29	Official Study Plan	2	-	6	48	162

Teaching language

Portuguese

Objectives

Acquisition of a solid foundation of knowledge in inductive statistics and development of skills and ingenuity in statistical modeling techniques, essential for the presentation, processing, and interpretation of data sets.

Learning outcomes and competences

Upon completing this course unit, the student should

- master the fundamental concepts and principles of Statistics, particularly basic Statistical Inference.

- be acquainted with and capable of applying the fundamental methods and techniques of parametric and non-parametric statistical inference to concrete problems, involving critical analysis and presentation of the results obtained.

- be able to use the R programming language in the statistical analysis of various types of data and problem-solving.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Probability Theory

Program

Parametric estimation: point and interval estimation.
Hypothesis tests.
Hypotheses tests for a given distribution. Hypotheses tests for normality. 
Parametric hypotheses tests for one sample: test for a single mean and test for a single proportion (exact test and approximation using the normal distribution).
Parametric hypotheses tests for two samples: difference of means and difference of proportions.  
Nonparametric tests alternative to the previous parametric tests.
Hypotheses tests (parametric and non-parametric) for more than two samples of a continuous random variable. Tests for categorical data. 
Pearson and Spearman correlation analysis and correspondent hypotheses tests.
Analysis of variance (ANOVA).
Point estimators and properties. Order statistics. Maximum likelihood estimation. 
Parametric hypotheses testing. Optimality criteria. Likelihood ratio tests.

Mandatory literature

Laura Cavalcante; Apontamentos disponibilizados pelo docente

Complementary Bibliography

Casella , George; Statistical inference. ISBN: 0-534-24312-6
James, Gareth,; An Introduction to Statistical Learning : With Applications in Python /. ISBN: 9783031387463
Nolan , Deborah; Stat labs : mathematical statistics through applications. ISBN: 0-387-98974-9
Rice , John A.; Mathematical statistics and data analysis. ISBN: 9780495118688

Teaching methods and learning activities

The program contents are generally presented in the theoretical classes, and some specific topics in the theoretical-practical classes, often using various examples to illustrate the topics covered and motivate students' independent study. Exercises and problems are solved and discussed in the theoretical-practical classes.


The introduction of statistical concepts and methods is supported by diverse examples, discussing the theoretical developments of the methods as well as their practical application and utilizing, when appropriate, the R software. Support materials are made available on the discipline's webpage.

Software

R

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Teste	100,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	106,00
Frequência das aulas	56,00
Total:	162,00

Eligibility for exams

unrestricted

Calculation formula of final grade

1. In the first call, the assessment will be based on two tests (with dates to be defined at the beginning of the semester). To be approved, a minimum rate of 25% is required in each of the tests. The final mark corresponds to the average of the marks obtained in the tests. 

2. In the second call, there will be an exam at the time of appeal (época de recurso), accessible to any student who has not passed in the regular time (época normal) or anyone aiming to improve their grade.

The students will be sucessful in the curricular unit once the final grade (obtained in the final examination) is greater than or equal to 9.5.

Students with a grade equal to or higher than 17.5 values may need to take an additional assessment to achieve a final grade equal to or higher than 18 values.

Classification improvement

All students will be able to improve their grade in the "epoca de recurso".