Statistics

Instance: 2013/2014 - 1S

Cycles of Study/Courses

Last updated on 2013-09-10.

Fields changed: Objectives, Resultados de aprendizagem e competências, Métodos de ensino e atividades de aprendizagem, Componentes de Avaliação e Ocupação, Provas e trabalhos especiais, Melhoria de classificação, Programa, Fórmula de cálculo da classificação final

Teaching language

Objectives

SPECIFIC AIMS:
Provide students with an integrated view of Statistics and of its usefulness, making them capacitated users of Descriptive Statistics and Statistical Inference.

Learning outcomes and competences

LEARNING OUTCOMES:
At the end of the semester, the students should be able to:
- Explain and interpret the main statistical concepts;
- Use descriptive statistics tools to analyse sample or populational data;
- Use excel spreadsheets to solve descriptive statistics problems.
- Solve common problems involving basic theory of probability, random variables, probability distributions, random sampling, confidence intervals and hypothesis testing.

Working method

Program

1. Introduction to Statistics: Scope and method;
2. Descriptive statistics: Description of univariate and bivariate samples of quantitative or qualitative data:
3. Basic probability theory;
4. Random variables and probability distributions: distributions of discrete and continuous variables, distribution parameters transformed variables;
5. Joint distribution of two random variables: joint, marginal and conditional distributions, independent variables, covariance and correlation, distribution of functions of two variables.
6. Probability distributions of discrete random variables: the Binomial distribution, the Hypergeometric distribution and the Poisson distribution.
7. Probability distributions of continuous random variables: the Uniform distribution, the Negative exponential distribution, and the Normal distribution, the t distribution, the Chi-square distribution and the F distribution;
8. Random sampling and sampling distributions: distribution of the sample mean. the Central limit theorem, Generation of random smaples;
9. Statistical inference: confidence intervals;
10. Statistical inference: hypothesis tests.
 
This course has a technological component of 10% and a scientific component of 90%.

Mandatory literature

Teaching methods and learning activities

Lectures - Tutorial: presentation of the themes of the course illustrated by cases, examples and solving of illustrative problems. Students can clarify possible doubts about the proposed problems.

Software

Evaluation Type

Assessment Components

Amount of time allocated to each course unit

Eligibility for exams

Calculation formula of final grade

The final grade (CF) is obtained by the following formula:

CF = 0.30 MT + 0.70 EF or (ER)

MT: Mini-exam performed in the middle of the semester in computer rooms.

EF: Final Exam.

ER: Appeal exam

The student has the option of discarding the classification obtained in the Mini-exam. In this case, the classification is obtained by:

CF = 1.00 EF (or ER)

Note: before the final exam the student must indicate whether the classification obtained in the Mini-exam will be discarded or not. Once discarded, the corresponding rating will not be considered for the final grade.

Examinations or Special Assignments

 There are no additional assignments.

Special assessment (TE, DA, ...)

Written Exam, weight 1.0.

Classification improvement

Global improvement: only by Exam, covering the all syllabus (weight: 1).