Statistics

Code:

EM0020

Acronym:

E

Keywords
Classification	Keyword
OFICIAL	Management

Instance: 2013/2014 - 2S

Active?	Yes
Web Page:	http://moodle.fe.up.pt
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Mechanical Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEM	232	Syllabus since 2006/2007	1	-	6	45,5	162

Teaching language

Portuguese

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

Presencial

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

Rui Campos Guimarães, José A. Sarsfield Cabral; Estatística. ISBN: 978-989-642-108-3
Wonnacott, Thomas H.; Introdução à estatística. ISBN: 85-216-0039-9

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

Microsoft Excel

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	100,00
Participação presencial	0,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	120,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

Students have to reach a minimum mark to be admitted to exams (See "General Evaluation Rules of FEUP - article 4).

 

Calculation formula of final grade

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

CF = 0.25 ME + 0.75 E 

ME: Mini-exam performed in the middle of the term in computer rooms to evaluate excel skills

E: Exam.

The Final Exam is credited for 100% and does not include the evaluation of excel skills.

 

 

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 all the syllabus (weight: 1).