Code: | EQ0076 | Acronym: | MAEQ |
Keywords | |
---|---|
Classification | Keyword |
OFICIAL | Physical Sciences (Mathematics) |
Active? | Yes |
Responsible unit: | Department of Chemical and Biological Engineering |
Course/CS Responsible: | Master in Chemical Engineering |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
MIEQ | 85 | Syllabus | 2 | - | 6 | 56 | 162 |
Framework:
The use of statistical analysis tools is undoubtedly an advantage to the improvement and quality of processes.
Specific aims:
- Acquisition of fundamental knowledge in statistics namely descriptive and inferential statistics enhancing the development of statistical literacy and reasoning
- Identification and formulation of problems of statistical analysis, its analytical resolution and computacional (through the use of application R®) fostering critical thinking.
Students should be able to:
1. Data organisation and sampling graphic visualization and classification of statistical data. Random selection (sampling).
2. Numerical description of data and expected values. Trend estimation (mean, mode, median). Variation estimation (standard deviation – variance, dispersion). Percentiles and quartiles. How to compare data apparently incomparable (z values)
3. Probabilities. The fundamental counting principle. Mutually exclusive and independent events. Dependent events- conditional probability (Bayes’ formula). Statistical expectation
4. Random variables. Distribution Function and Probability Density Function. Joint distributions Conditional distributions Covariance and correlation
5. Discrete probabilities distributions: Uniform, Binomial, Hypergeometric and Poisson.
6. Continuous probability distributions: Uniform distribution. Normal distribution. Description and applications (rejection of outliers). Normality tests- graphic approximation (probit scale).
7. Sample distributions. Mean distribution and Central Limit Theorem. Chi-square, T-Student and F-Snedcor distribution
8. Estimators and Moment Generating Function.
9. Confidence intervals and hypothesis testing. T test, F test. Means, proportions and variance. How to estimate sample sizes.
10. Analysis of regression and experimental data. Simple linear regression. Standard error of estimate and residual variance. Regression parameters. Problem and meaning of correlation coefficient.
11. Analysis of Variance (ANOVA): One and two factors. Applications
12. Quality control
TP - Theoretical-practical classes of 90 + 60 minutes for presentation of the main concepts together with problem solving.
L - Laboratory classes of 90 minutes in computer rooms solving problems with or without the use of R + R Commander.
Designation | Weight (%) |
---|---|
Exame | 75,00 |
Teste | 25,00 |
Total: | 100,00 |
Obtaining attendance for regular students depends on:
Students with frequency from previous years do not need to attend classes. Students who wish to attend must obey the above rules.
Students who comply with point 1 but not point 2. may attend the examination in Recurso season, thus obtaining attendance at the UC.
The final classification (CF) is calculated by the equation:
CF = max(0.25 x AD + 0.75 x EF,EF)
wherein:
AD = arithmetic average of the three best marks obtained in mini-tests
EF = marks obtained in the open recommended book Final Exam, with partial use of the R Commander
The final exam consists in two parts: theorethical (TP) and pratical (P). The final mark is calculated by the equation:
EF = 0.6 x TP + 0.4 x P
Conditions for course approval:
The realization of the mini-tests is mandatory for all students without previous frequency. The other may choose as venue and must communicate its decision to the teacher in the first week of classes. Not performing a mini-test on the date set corresponds to zero mark.
An exam at the corresponding seasons.
Improvement of classification can be attempted in the Recurso season. The computation formula is identical to the one used in final classification described above.