| Competences |
| Type A | Code | Competences Specific |
| A1 | Apply basic knowledge of mathematics and physics at the molecular biosciences | |
| A8 | Analyse appropriately data and experimental results from the fields of biotechnology with statistical techniques and be able to interpret it. | |
| Type B | Code | Competences Transversal |
| Type C | Code | Competences Nuclear |
| Learning outcomes |
| Type A | Code | Learning outcomes |
| A1 |
Know how to apply mathematical estimation and statistical contrasts in decisions regarding the values and margins of error of physical or chemical parameters Know how to apply statistical concepts and techniques to the treatment of experimental results in order to estimate the reliability of the final values. Know how to formulate models for adjusting experimental results to physical and chemical theoretical functions. Use ICT tools for the statistical handling of data. | |
| A8 |
Understand the basic principles of the models of continuous and discrete probability distribution. Know how to apply mathematical estimation and statistical contrasts in decisions regarding the values and margins of error of physical or chemical parameters Know how to apply statistical concepts and techniques to the treatment of experimental results in order to estimate the reliability of the final values. Know how to formulate models for adjusting experimental results to physical and chemical theoretical functions. Use ICT tools for the statistical handling of data. | |
| Type B | Code | Learning outcomes |
| Type C | Code | Learning outcomes |
| Contents |
| Topic | Sub-topic |
| 1. Introduction to data analysis. | 1.1. Concept of Statistics. Contents of Statistics. 1.2. Concept of population, sample, individual and random variable. 1.3. Classification of the statistical variables. 1.4. Position parameters. 1.5. Dispersion parameters. |
| 2. Random variables. | 2.1. Concept of probability and properties. 2.2. Concept of random variable. 2.3. Discrete random variables: probability function and distribution function. 2.4. Continuous random variables: density function and distribution function. 2.5. Expected value. 2.6. Variance. |
| 3. Models of probability distribution. | 3.1. Discrete distributions: Bernoulli, binomial, Poisson, uniform. 3.2. Continuous distributions: uniform, exponential, normal. 3.3. General normal law. Reduced normal law: N(0,1). 3.4. Distributions deduced from the normal: khi-squared, Student’s t and Snedecor’s F. 3.5. Convergence to the normal law: central limit theorem. 3.6. Use of statistical tables. |
| 4. Theory of estimation. | 4.1. Concept of estimator and parameter. Point estimation and interval estimation. 4.2. Properties of estimators: bias, efficiency and consistency. 4.3. Some methods of estimation: method of moments and method of maximum likelihood. 4.4. Notion of confidence interval. Confidence coefficient. 4.5. Determination of confidence intervals for: a mean, a difference between means, a variance, a ratio between variances, a proportion and a difference between proportions. |
| 5. Hypothesis testing. | 5.1. Statistical hypotheses. Types of hypotheses. 5.2. Concept of critical region and acceptance region. 5.3. Types of errors. Power of a test. Significance level. 5.4. Applying hypothesis testing to: a mean, a difference between means, a variance, a ratio between variances, a proportion and a difference between proportions. |
| 6. Analysis of variance. | 6.1. General concepts about the analysis of variance. 6.2. One-way design. 6.3. Two-way design without interaction. Random blocks. 6.4. Two-way design with interaction. |
| 7. Linear regression. | 7.1. Simple linear regression model. 7.2. Estimation of the regression line by the least squares method. 7.3. Goodness-of-fit measures. 7.4. Significance testing. 7.5. Prediction intervals. 7.6. Non linear regression. 7.7. Multiple linear regression. |
| 8. Numerical methods. | 8.1. Error analysis. 8.2. Zeros of functions. 8.3. Solving systems of linear equations. 8.4. Numerical integration. 8.5. Numerical solution of differential equations. |
| Planning |
| Methodologies :: Tests | ||||||||||
| Competences | (*) Class hours |
Hours outside the classroom |
(**) Total hours | |||||||
| Introductory activities |
|
1.2 | 0 | 1.2 | ||||||
| Lecture |
|
28 | 44.8 | 72.8 | ||||||
| IT-based practicals in computer rooms |
|
28 | 42 | 70 | ||||||
| Personal attention |
|
0 | 0 | 0 | ||||||
| Short-answer objective tests |
|
3 | 3 | 6 | ||||||
| (*) On e-learning, hours of virtual attendance of the teacher. (**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||||||||
| Methodologies |
| Description | |
| Introductory activities | Introduction of the course explaining the contents to develop, the objectives to evaluate, the methodology used and the evaluation method. |
| Lecture | The professor explains the theoretical content of each subject. A whiteboard and the projection of notes are used. |
| IT-based practicals in computer rooms | Students are asked to solve and deliver practical exercises, using a computer, related to the content they are currently working on. These practical exercises are part of the ongoing evaluation of the course. |
| Personal attention | Students can enjoy personalized attention for any aspect of the course during the hours of personal tuition and the hours of problem solving and practical classes. |
| Personalized attention |
| Description |
|
Time reserved for individual attention and doubt solving with students. Due to the health emergency, this attention can be carried out through online meetings, previously appointed by e-mail, or with other online tools. |
| Assessment |
| Methodologies | Competences | Description | Weight | ||||
| IT-based practicals in computer rooms |
|
Students, with the help of the professor, have to solve problems about several course contents. The practical exercises will be assessed. |
50% | ||||
| Short-answer objective tests |
|
Individual final exam of synthetic type. The only material allowed to be used will be the following: a scientific calculator, statistical tables and a form with a maximum of 3 sheets. | 50% | ||||
| Others |
| Other comments and second exam session | |||
|
The exams will be held in person. In case of lockdown or mobility restrictions caused by the Covid-19 health emergency, the assessment activities, including exams, would be done online on the scheduled dates. Updated information can be found on Moodle (virtual teaching space). The second call consists in an individual final exam of a synthetic nature. The practices grade is saved if it is higher than or equal to 5 (in this case, the practices grade and the exam grade weigh 50% each). If the practices grade is less than 5, then this grade is not saved and the exam weighs 100%. During the evaluation tests, mobile phones, tablets and other devices that are not expressly authorized for the test, must be off and out of sight. The demonstrable fraudulent realization of some evaluation activity in both material and virtual and electronic support leads the student to the suspense note of this evaluation activity. Regardless of this, given the seriousness of the facts, the Faculty may propose the initiation of a disciplinary proceeding, which will be initiated by resolution of the rector. |
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| Sources of information |
| Basic |
Mateo, J.M., Estadística pràctica pas a pas, , URV |
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| Complementary | |||||||||
| Recommendations |
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.