Statistics applied to science and engineering
### Working language
English
_Note: Note that the working language will be English, and students can always ask questions in Portuguese._
### Goals
1\. Enable the student for regression analysis involving continuous or discrete responses (generalized linear models)
two\. Implement statistical analyzes in suitable software
3\. Promoting a critical spirit in a data analysis process (data collection, modelling, interpretation of results, ...)
### Learning outcomes and skills
At the end of the curricular unit, it is intended that students:
a) acquire knowledge about the organized collection of information
b) learn statistical techniques and models commonly used in data processing
c) know how to correctly choose the statistical models learned for concrete problems
d) know how to apply and implement the models studied in R
e) acquire a critical spirit and ability to interpret the results obtained.
### Working mode
In person
### Prerequisites (prior knowledge) and co-requisites (concurrent knowledge)
Prior knowledge of random variables and probability distributions, sample statistics, confidence intervals and hypothesis testing is required. These are the usual contents of an introductory curricular unit to Probability and Statistics in higher education. A brief review of this matter will be carried out.
### Program
0\. Brief review of inference-based techniques. statistics - confidence intervals and hypothesis tests
1- Introduction to programming language in software environment **R.**
two\. Pearson correlation and Spearman correlation.
3\. Simple linear regression.
4\. Multiple linear regression. Model, parameter estimation, hypothesis tests for coefficients, confidence intervals, prediction intervals, determination coefficient, multicollinearity, model selection methods, model comparison, diagnosis.
5\*. Analysis of variance - ANOVA: 1 and 2 factors.
6\*. Generalized linear models. Logistic regression.
\*Only one subject will be studied, from 5. to 6.
### Mandatory Bibliography
Rita Gaio; Notes written by the teacher
### Complementary Bibliography
ISBN: 1-58488-029-5
ISBN: 0-387-95475-9
ISBN: 978-0-521-86116-8
ISBN: 0-387-95187-3
ISBN: 0-387-95284-5
ISBN: 1-58488-325-1
ISBN: 0-387-98218-3
Julian Faraway; Linear Models with R, Taylor and Francis, 2009. ISBN: 1584884258
Julian Faraway; Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall/CRC Texts in Statistical Science, 2006. ISBN: 158488424X
### Teaching methods and learning activities
Theoretical-practical classes with different examples of application of techniques and statistical models presented in a computational laboratory. The software used is R.
### Software
R Project
### Key words
Physical Sciences > Mathematics > Statistics
### Type of evaluation
Distributed evaluation with final exam
### Assessment Components
Test: 37.50%
Written work: 25.00%
Exam: 37.50%
**Total:**: 100.00
### Occupation Components
Self-study: 110.00 hours
Frequency of classes: 42.00 hours
Written work: 10.00 hours
**Total:**: 162.00
### Get Frequency
There is no lack of frequency.
### Final classification calculation formula
1\. The work consists of a written report and an oral presentation. Carrying out the work is optional.
two\. The grade of the work cannot be improved.
3\. The evaluation of the normal season will include the classification of two tests (T1 and T2), each with a quotation of 10 points. The T2 test will take place on the day designated for the exam of the normal season.
4\. The evaluation of the appeal period will only include a final exam, which will focus on all the contents of the curricular unit. The classifications of the T1 and T2 tests will not be considered here.
5\. Evaluation formula in the **regular season**: There are two evaluation formulas, depending on whether or not the curricular unit's work/project is delivered.
a) For students who **turn in work**:
a1) T1+T2: weight of 13 or 15 (in 20); work: weight of 7 or 5 (in 20)
Of the two evaluation components, the one in which the student had the best rating (on a scale of 0-20) has, for that student, the maximum weight indicated above. The worst component has, for that student, the minimum weight indicated above.
a2) In order to pass, the student must obtain a classification greater than 20% in each of the components (tests and work).
b) For students who **do not turn in the work**:
In this case, only the test scores count; however, the student's final classification will never be higher than 16, even with a higher grade in the tests.
6\. Evaluation formula in **appeal season**: There are two evaluation formulas, depending on the delivery or not of the work/curricular unit project.
a) For students who **turn in work**:
a1) resource exam: weight of 13 or 15 (out of 20); work: weight of 7 or 5 (in 20)
Of the two evaluation components, the one in which the student had the best rating (on a scale of 0-20) has, for that student, the maximum weight indicated above. The worst component has, for that student, the minimum weight indicated above.
a2) In order to pass, the student must obtain a classification greater than 20% in each of the components (exam and work).
b) For students who **do not turn in the work**:
In this case, only the exam score counts; however, the final classification of the student will never exceed 16 points, even if he/she has a higher grade in the exam.
More information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479406
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Statistics applied to science and engineering
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