Type A
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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
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Code |
Competences Transversal |
Type C
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Code |
Competences Nuclear |
Type A
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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.
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Type B
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Code |
Learning outcomes |
Type C
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Code |
Learning outcomes |
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Methodologies :: Tests |
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Competences |
(*) Class hours
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Hours outside the classroom
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(**) Total hours |
Introductory activities |
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1.2 |
0 |
1.2 |
Lecture |
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28 |
44.8 |
72.8 |
Practicals using information and communication technologies (ICTs) in computer rooms |
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28 |
42 |
70 |
Personal tuition |
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0 |
0 |
0 |
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Objective short-answer tests |
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3 |
3 |
6 |
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(*) 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
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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. |
Practicals using information and communication technologies (ICTs) 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 tuition |
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. |
Description |
The students can enjoy personalized attention for any aspect of the course during the personal tuition and problem solving and practical classes hours. |
Methodologies |
Competences
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Description |
Weight |
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Practicals using information and communication technologies (ICTs) in computer rooms |
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Students, with the help of the professor, have to solve problems about several course contents. The practical exercises will be assessed.
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50% |
Objective short-answer tests |
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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 |
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Other comments and second exam session |
In the second call there will be an individual final examination of a synthesis nature where you can only carry and consult the following material: scientific calculator, statistical tables and a form of a maximum of 3 sheets. The practical note is saved if it is more than 5 (in this case, the mark of the practices and the one of the examination weigh 50% each one). If the mark of practices is less than 5, this note is not saved and the exam will be 100%. The final mark of the 2nd call will be a maximum of 5, because of the different difficulty with regard to the examination of the 1st call . 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 of a subject both in material and virtual and electronic support entails the student's suspense note of this evaluation activity. Regardless of this, given the seriousness of the events, the center may propose the initiation of a disciplinary file, which will be opened by resolution of the rector. |
Basic |
Mateo, J.M., Estadística pràctica pas a pas, , URV
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Complementary |
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(*)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. |
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