Statistics

Code:

EM0020

Acronym:

E

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2012/2013 - 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	250	Syllabus since 2006/2007	2	-	6	56	160

Teaching language

Portuguese

Objectives

SPECIFIC AIMS:
Provide students with an integrated view of Statistics and of its usefulness, making them potential 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
- Solve common problems involving basic theory of probability, random variables, probability distributions, random sampling, confidence intervals and hypothesis testing
- Use spreadsheets to solve descriptive statistics problems

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.
11. Analysis of Variance: fixed effects, one factor.

 

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
Wonnacott, Thomas H.; Introdução à estatística. ISBN: 85-216-0039-9

Teaching methods and learning activities

Lectures: presentation of the themes of the course illustrated by cases, examples and problems

Tutorial classes: Students can solve and discuss practical exercises and clarify possible doubts about proposed problems.

Software

Microsoft Excel
Microsoft Excel

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	56,00
Assessment	Exame	4,50	100,00
	Total:	-	100,00

Amount of time allocated to each course unit

Description	Type	Time (hours)	End date
Problem solving training	Estudo autónomo	47
Learning theoretical concepts	Estudo autónomo	59
	Total:	106,00

Eligibility for exams

Article 4 of General Evaluation Rules of FEUP

Calculation formula of final grade

Final grade (CF) is obtained by the following formula:

CF = 0.20 MT1 + 0.30 MT2 T2 + 0.50 MT2 T3

MT1, MT2, MT3: Mini-exams held furing the semester in computer rooms. In case the studentt fails during the distributed evaluation, he can get course approval in the final exam (weight 1).

Examinations or Special Assignments

 

Special assessment (TE, DA, ...)

Written Exam, weight 1.0.

Classification improvement

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