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Presentation
Presentation
This subject is devoted to fundamental concepts in the theory of probability, statistics and statistical inference
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Master Degree | Semestral | 7
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT6347-25229
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
1. Descriptive Statistics
Types of data: integers, continuous, categorical, arrays, matrices
Frequency tables
Measures of central tendency and variability
Visualization
2. Linear Regression
Independent vs. dependent variables. Scatter plots
Covariance and Pearson coefficient
Regression line, residuals, least squares method
Calculating the estimate for the response given a certain value for the independent variable
3. Probability
Random experiment. Sample space. Event. Operations between events
Properties of the probability function. Probability of the union of events
Law of total probability
Bayes' theorem
Conditional probability. Independent events
4. Statistical Inference
Sample and random sample
Estimator and estimate for a proportion
Hypothesis test for a proportion
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Objectives
Objectives
This subject aims to show that
LG1: probability is as an essential measure function in science.
LG2: statistics enables us to collect data, analyse data, establish hypothesis on data and test these hypothesis. Hence, both probability and statistics lead us to knowledge in science and engineering.
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Teaching methodologies and assessment
Teaching methodologies and assessment
In class, the ideas that underpin the program of this Curricular Unit (CU) are discussed, and multiple examples and application exercises are analyzed.
For each topic of this CU, a set of application exercises is presented. Students are encouraged to solve these exercises and to present any doubts they may have.
This CU will share content and support material with another CU (Introduction to Data Science).
All support material and relevant information will be shared with the students through Moodle.
The evaluation includes a continuous component, which consists of three 20-minute mini-tests (whose average corresponds to 30% of the final grade) and one exam (which accounts for 70% of the final grade). Students who obtain a final grade of no less than 10 points are considered to have passed.
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References
References
- Morais, M. C. (2020): Probabilidades e Estatística: Teoria, Exemplos e Exercícios, IST Press (Coleção Ensino da Ciência e da Tecnologia)
- Murteira, B., Ribeiro, C.S., Andrade e Silva, J., e Pimenta C. (2010): Introdução à Estatística, Escolar Editora
- Murteira, B. (1993): Análise Exploratória de Dados - Estatística Descritiva, McGraw-Hill
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Office Hours
Office Hours
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Mobility
Mobility
No