Probability and Statistics
Code: 100965 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2500253 Biotechnology | FB | 2 | 1 |
Contact
- Name:
- Clara Mateo Campo
- Email:
- Clara.Mateo@uab.cat
Use of Languages
- Principal working language:
- catalan (cat)
- Some groups entirely in English:
- No
- Some groups entirely in Catalan:
- Yes
- Some groups entirely in Spanish:
- No
Teachers
- Clara Mateo Campo
Prerequisites
The first course of Mathematics guarantees knowledge required by this course.
Objectives and Contextualisation
Statistics is the technology of the scientific experimental method (Mood, 1972).
The aim of the course is to introduce the fundamental tools of probability and statistical inference to analyze biological data from the description of natural phenomena or experiments, focusing on the proper use and interpretation of results.
Competences
- Make decisions.
- Reason in a critical manner
- Use the fundamental principles of mathematics, physics and chemistry to understand, develop and evaluate a biotechnological process.
Learning Outcomes
- Analyse the relationship between variables using techniques for analysing variance, linear and non-linear regression, and correlation.
- Correctly adjust experimental measurements for linear and non-linear regression.
- Describe the basic properties of point estimators and interval estimators. Formulate and solve hypothesis contrast problems in one or two populations.
- Explain the principles behind the theory of probability that underlie inferential statistics and recognise real situations in which the most common probabilistic distributions appear.
- Make decisions.
- Reason in a critical manner
Content
1. Descriptive Statistics of one and two variables
- Descriptive statistics (mean and standard deviation, range, median and quartiles, covariance and correlation coefficient).
- Graphic representations.
- Descriptive bi-variant.
2. Probability and random variables
- Notion of probability. Conditional probability. Independent events.
- Random Variable. Expectation and variance. Independent random variables.
- Classical discrete distributions: Bernoulli, Binomial, Poisson ...
- Classical continuous distributions: Uniform, Exponential, Normal and derived distributions.
3. Statistical inference in data analysis
- Population and sample. Statistics: mean, variance and sample proportion.
- inference: estimation and confidence intervals.
- Testing hypotheses.
- Comparison of two populations.
- Analysis of variance of a factor.
4. The simple linear regression model
- The estimate of the least squares regression line,
- Test the relationship between the variables.
- Confidence intervals for the prediction.
Methodology
Lectures:
The concepts of the subject will be present. It will be focus on the results and interpretation of the relationship between these concepts and their applications. Examples that allow students to deal independently solving problems will be shown.
Classes of problems:
Students will have a list of problems in the course, which will work progressively.
Independent activities:
Individual study of the theory: the reflection and deepening of the subject introduced by class notes and bibliography must be addressed.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Classes of problems | 16 | 0.64 | 2, 1, 3, 4, 5, 6 |
| Lectures | 32 | 1.28 | 2, 1, 3, 4, 5, 6 |
| Type: Autonomous | |||
| Problem solving | 64 | 2.56 | 2, 1, 3, 4, 5, 6 |
| Study of the theory | 30 | 1.2 | 2, 1, 3, 4, 5, 6 |
Assessment
Attendance to practical sessions (or field trips) is mandatory. Students missing more than 20% of programmed sessions will be graded as "No Avaluable
hroughout the course, the following five assessment tests will be carried out:
Moodle (3%)
Test theory 1 (10%).
Test problems 1 (40%).
Theory test 2 (10%).
Test problems 2 (40%).
The weighted average of the five tests will be the qualification, but a minimum of 3 (out of 10) must be taken in the two tests of problems to be able to make the average.
To be eligible for the retake process, the student should have been previously evaluated in a set of activities equaling at least two thirds of the final score of the course or module. Thus, the student will be graded as "No Avaluable" if the weighthin of all conducted evaluation activities is less than 67% of the final score
If the approved one is not reached, the four partial tests can be recovered together in order to achieve this qualification.
The "Not evaluable" qualification will be obtained if the student does not submit to any evaluation activity.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| E-learning Moodle tool | 3% | 0 | 0 | 2, 1, 3, 4 |
| partial practical test 1 | 40% | 3 | 0.12 | 2, 1, 3, 4, 5, 6 |
| partial theoretical test 1 | 10% | 1 | 0.04 | 2, 1, 3, 4, 5, 6 |
| partial theoretical test 2 | 40% | 3 | 0.12 | 2, 1, 3, 4, 5, 6 |
| partial theoretical test 2 | 10% | 1 | 0.04 | 2, 1, 3, 4, 5, 6 |
Bibliography
Daniel, W.(1987). Bioestadística. Base para el análisis de las ciencias de la salud, Limusa.
D. Peña. (2001). “Fundamentos de Estadística”. Alianza Editorial.
D. Peña. (2002). “Regresión y diseño de experimentos”. Alianza Editorial.
- Milton, J. S. “Estadística para Biología y Ciencias de la Salud”. Interamericana de España, McGraw-Hill, 1994 (2a ed.).
- Zaiats, V. Calle, M.L., Presas, R. “Probabilitat i Estadística. Exercicis I”. Materials 107. Servei de publicacions de la UAB, 2001.
- Zaiats, V. Calle, M.L. “Probabilitat i Estadística. Exercicis II”. Materials 108. Servei de publicacions de la UAB, 2001.
- Montgomery, D. C. “Diseño y análisis de experimentos” (2a. ed.) Limusa-Wiley, 2002.