Statistics I

Code:

EIG0015

Acronym:

E I

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2011/2012 - 1S

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Engineering and Industrial Management

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEIG	80	Syllabus since 2006/2007	2	-	6	56	160
PRDEIG	0	Syllabus since 2007/08	1	-	6	56	160

Teaching language

Portuguese

Objectives

AIMS
This course unit aims to acquaint students with underlying knowledge on Descriptive Statistics, Probability Theory, Probability Distributions, Random Sampling, Sampling Distribution and Point and Interval Estimates. Later on, when attending to the course unit Statistics II, students will be asked to recall this knowledge in order to learn statistics techniques, which will have an important application in their future career.


LEARNING OUTCOMES
At the end of the semester, students should be capable of: (I) identifying the concepts of this course unit in a structured way; (II) using tools of descriptive statistics in the analysis of data samples; (III) solving common problems, which involve elementary probability theory, random variables, probability distributions and point and interval estimation; (IV) using Microsoft Excel to solve the above mentioned problems.

Program

SCOPE AND METHOD OF STATISTICS;
DESCRIPTIVE STATISTICS: univariate and bivariate samples involving quantitative and qualitative data; ELEMENTARY PROBABILITY THEORY; RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS: Distribution of discrete and continuous variables; Distribution parameters; Transformed variables; JOINT DISTRIBUTION OF TWO RANDOM VARIABLES: Joint, marginal and conditional distributions; Independent variables; Covariance and correlation; Distribution of functions of two or more random variables; CHARACTERISTICS OF SOME UNIVARIATE DISCRETE DISTRIBUTION: Binomial, Negative Binomial, Hypergeometric, and Poisson distributions. CHARACTERISTICS OF SOME UNIVARIATE CONTINUOUS DISTRIBUTIONS: Uniform, Negative Exponential, Normal, Chi-square, t and F distributions. RANDOM SAMPLING AND SAMPLING DISTRIBUTIONS: Distribution of a sample average; Central limit theorem; Sample generation using Monte Carlo technique; POINT ESTIMATION: Estimators and estimates; Desirable properties of point estimators; estimation methods. INTERVAL ESTIMATION: Concept of confidence interval; Specification of confidence intervals; Selection of sample sizes.

Mandatory literature

Guimarães, R. M. C. e J. A. Sarsfield Cabral; Estatística, Verlag Dashöfer Portugal, 2010. ISBN: 978-989-642-108-3

Teaching methods and learning activities

The methods and techniques are introduced using systematically practical examples. The learning process is complemented with problem solving sessions supported by computer software and with 2 team work assignments.

Software

SPSS
Folha de Cálculo
R
SOFA

keywords

Physical sciences > Mathematics > Statistics
Physical sciences > Mathematics > Probability theory

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	56,00
Final exam	Exame	30,00		2012-02-10
	Total:	-	0,00

Amount of time allocated to each course unit

Description	Type	Time (hours)	End date
Individual study	Estudo autónomo	74	2011-12-16
	Total:	74,00

Eligibility for exams

Admission criteria set according to Article 4 of General Evaluation Rules of FEUP.

Calculation formula of final grade

Assessment will take place through 2 mini-tests (MT) and 2 team work (TW) assignments. The final mark (FM) will be obtained by the following formula:
FM = 0.40 MT1 + 0.40 MT2 + 0.10 TW1 + 0.10 TW2
Students have to reach a minimum final mark of 10 out of 20 and a minimum mark of 7 out of 20 in each of the mini-tests.

Examinations or Special Assignments

None.

Special assessment (TE, DA, ...)

Special evaluations will be made by a final exam.

Classification improvement

At the end of the of the course unit, a final exam will take place. In such exam, students can either complete the course unit or improve their grades.