Code: | EIG0015 | Acronym: | E I |
Keywords | |
---|---|
Classification | Keyword |
OFICIAL | Mathematics |
Active? | Yes |
Responsible unit: | Department of Industrial Engineering and Management |
Course/CS Responsible: | Master in Engineering and Industrial Management |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
MIEIG | 89 | Syllabus since 2006/2007 | 2 | - | 6 | 56 | 162 |
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.
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 spreadsheets to solve the above mentioned problems.
Basic spreadsheets skills.
1. Introduction to Statistics: Scope and method.
2. Descriptive statistics: Description of univariate and bivariate samples.
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: Binomial, Hypergeometric and Poisson distributions.
7. Probability distributions of continuous random variables: Uniform, Negative exponential, Normal, t, Chi-square and F distributions.
8. Random sampling and sampling distributions: distribution of the sample mean, the Central limit theorem, Generation of random samples.
9. Point estimation: estimators and estimates.
10. Confidence intervals.
The methods and techniques are introduced using systematically practical examples. The learning process is complemented with problem solving sessions supported by computer software and teamwork assignments.
Designation | Weight (%) |
---|---|
Exame | 65,00 |
Teste | 20,00 |
Trabalho escrito | 15,00 |
Total: | 100,00 |
Designation | Time (hours) |
---|---|
Elaboração de relatório/dissertação/tese | 10,00 |
Estudo autónomo | 66,00 |
Frequência das aulas | 56,00 |
Trabalho laboratorial | 30,00 |
Total: | 162,00 |
Admission criteria set according to Article 4 of General Evaluation Rules of FEUP.
The final mark (CF) will be obtained by the following formula:
CF = 0.20 FA + 0.15 TG + 0.65 EF
FA - Quizzes:
- 6 quizzes (pratical classes);
- the quizzes mark (FA) is obtained by the average of the best 4 marks achieved by each student.
TG - Teamwork assignments:
- 2 small size teamwork assignments (TG1 e TG2).
EF - Final Exam
- open book exam.
To pass this course, apart from a final grade no less than 10, is required a minimum grade of 7 in the final exam.
Special evaluations will be made by a final exam (closed book).
Students may choose between:
- improving the components Quizzes (FA) and Final Exam (EF);
- improving only the component Final Exam (FE).
The component teamwork assignments (TG) is possible to improve.