Code: | M2015 | Acronym: | M2015 | Level: | 200 |
Keywords | |
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Classification | Keyword |
OFICIAL | Mathematics |
Active? | Yes |
Responsible unit: | Department of Mathematics |
Course/CS Responsible: | Bachelor in Chemistry |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
L:B | 0 | Official Study Plan | 3 | - | 6 | 56 | 162 |
L:CC | 0 | study plan from 2021/22 | 2 | - | 6 | 56 | 162 |
L:F | 0 | Official Study Plan | 2 | - | 6 | 56 | 162 |
L:G | 0 | study plan from 2017/18 | 2 | - | 6 | 56 | 162 |
3 | |||||||
L:Q | 45 | study plan from 2016/17 | 2 | - | 6 | 56 | 162 |
3 |
To show how statistical reasoning is used in sciences research and to enable students to perform simple statistical analyzes and to interpret the results. Particular attention is paid to the understanding of concepts and to the critical use of methods, while maintaining mathematical treatment at an elementary level.
1. Be able to identify the techniques of descriptive statistics appropriate to organize and summarize a data set and to carry a basic data exploration using software R.
2. Dominate the fundamental concepts of probability calculus and know to calculate probabilities associated with the phenomenon under study.
3. Be able to characterize random variables and their probability distributions. Understand the characteristics and know how to apply the binomial and normal distributions in the modeling of processes.
4. Infer about the characteristics of a population based on a sample using point and interval estimation.
5. Understand the general procedures and know how to select, apply and interpret hypothesis tests.
1. Brief introduction to the objectives and methodology of statistics.
2. Descriptive Statistics and exploratory data analysis: summarizing data (tables, graphs, measures of location and dispersion) using R.
3. Probability: basic concepts and properties, conditional probability and independence.
4. Random variables: discrete and continuous, probability distributions, expected value and variance, binomial and normal probability distributions, assessing normality.
5. Sampling distributions and central limit theorem.
6. Statistical inference: interval estimation (mean, difference of means, proportion, difference of proportions), hypothesis testing: t, chi-square, one-way ANOVA.
The contents of the syllabus are mainly presented in the lectures, providing examples in order to illustrate and motivate the concepts and methods considered. Some specific topics are only presented in the classes, where exercises and related problems are solved and discussed.
All resources are available for the students at the unit’s web page.
designation | Weight (%) |
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Teste | 100,00 |
Total: | 100,00 |
designation | Time (hours) |
---|---|
Estudo autónomo | 106,00 |
Frequência das aulas | 56,00 |
Total: | 162,00 |
1. In the first call the final mark will be the sum of the scores obtained in two assessments:
Assessment 1: with a total of 8 points, will take place in a date to be settled with students.
Assessment 2: with a total of 12 points, will take place during the period settled for conclusion of distributed evaluation.
2. In the second call, the final mark will be obtained in an exam with a total of 20 points.
This exam will be divided in two parts, allowing the students which have not yet been aproved in the course (and only these students) to substitute the score in any of the two parts by the score obtained in the corresponding assessment.