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Statistics

Code: 103240
Credits: 6
2026/2027
Degree programme Type Course
Food Science and Technology FB 1

Contact lecturer

Name :
Mercè Farre Cervello
Email :
merce.farre@uab.cat

Teaching staff

Niels Knudsen Esquerda

Group languages

You can consult this information at the end of the document.

Prerequisites

Although there are no formal prerequisits, it is recommended that the student reads up on:

1)     Elementary combinatorics and the Newton binomial.

2)     The probability theory and statistics studied in high school.

3)     Elementary functions (exponential, logarithm), summations.

It is also recommended that the student follows simultaneously the course Mathematics.

Objectives

Context:

This is a basic course,introducing the tools of probability theory and statistics. Together with the course in mathematics, it also helps students develop scientific rigour and logical thinking.

Objectives:

1)     fluency in the language of probability and statistics.

2)     familiarity with descriptive methods in connection with data sets resulting from experiments.

3)     ability to choose models approriately.

4)     familliarity with the concept of random variable, the basic distributions, and which situations they serve.

5)     methods of statistical inference.

6)     statistics software. 

7)     critical spirit when faced with problems, modelling, conclusions, and decision making.

Learning outcomes

  1. Analyse, summarise, resolve problems and make professional decisions.
  2. Apply the scientific method to resolving problems.
  3. Design experiments and interpret the results.
  4. Search for, manage and interpret information from different sources.
  5. Use IT resources for communication, the search for information within the field of study, data processing and calculations.
  6. Identify and select the most important information sources for the descriptive analysis of data of different types: environmental, healthcare, economic, etc.
  7. Clean up data: lost data, transformation of variables, anomalous data, case selection and other techniques that precede statistical analysis.
  8. Explore behaviour patterns of univariate and bivariate data.
  9. Describe, using the appropriate graphic and analytical methods, qualitative data on one or more variables.
  10. Describe, using the appropriate graphic and analytical methods, quantitative data on one or more variables.
  11. Use spreadsheets for the descriptive analysis of data.
  12. Use specific statistical software for the descriptive analysis of data.
  13. Validate and manage information to be processed statistically.
  14. Summarise and discover behaviour patterns in data exploration.
  15. Use univariate and bivariate summary indices.
  16. Identify statistical distributions.
  17. Use the properties of distribution functions.
  18. Use the properties of density functions.
  19. Interpret the results obtained and draw conclusions regarding the experimental hypothesis.
  20. Use statistical inference as an instrument for making predictions.
  21. Describe the basic properties of point estimators and interval estimators.
  22. Analyse data using statistical methods and techniques, working with qualitative and quantitative data.
  23. Use statistical software to manage databases.
  24. Use statistical software to obtain summary indices of the variables in the study.
  25. Use statistical software to analyse data using inference techniques.

Contents

1. Descriptive statistics


Data and error. Descriptive analysis of data from one random variable. Disitributions, ffreequencies, graphical representation, numerical summaries (position, dispersion, form). Descriptive analysis of data from two random variables: correlation, regression line, cross tables.


2. Probability


a)     Basic properties of probablity. Conditional probability. Total probability and Bayes' formula.
b)    Discrete random variables\" Bernoulli, Binomial, Poisson.
c)     Continuous random variables. The normal distribuion.


3. Statistics


a)     Introductino to Statistics: població and sample, parameters and estimators, independen variables. Sample mean distribution in the normal case with known variance. The Z-statistic. Cofidence interval for the mean of a normal distribution with known variance.
b)    Student t-distribution. Case of unknown variance: T-statistic. Confidence interval for the mean of a normal distribution with unknown variance. Sample proportion. Assymptotic interval for a propoertion.
c)     Introduction to hypothesis testing. Hypothesis test for the mean of a normal distribution with known variance. Hypothesis test for the mean of a normal distribution with unknown variance. Hypothesis test for a proportion. Test for comparing two means.
d)    Goodness of fit test with chi square. Test for independence, test for homogeneity.


 


 

Learning activities and methodology

Title Hours ECTS Learning outcomes
Theory 22 0.88 1, 2, 3, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22
Tutorials 10 0.4 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
Self-study and problem solving 79 3.16 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25
Problem classes 13 0.52 1, 2, 3, 4, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 22
Computer lab classes 15 0.6 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 22, 23, 24, 25
  • Theory classes:

lectures

  • Problem classes:

individual or group work, under the supervision of the teacher.

  • Computer lab classes:

Individual work with a computer, solving exercises.

 

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Computer lab 20% 1.5 0.06 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 19, 22, 23, 24, 25
Second written exam 45% 3 0.12 1, 2, 3, 4, 8, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22
First written exam 35% 3 0.12 1, 2, 3, 4, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22
Recovery exam 80% 3.5 0.14 1, 2, 3, 4, 8, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22

THE CATALAN VERSION IS THE OFFICIAL. THE FOLLOWING IS A SUMMARISED TRANSLATION ONLY.

Two written exams during the semester, together accounting for 80% of the final grade. Further 20% for the computer lab sessions.

To pass the course, a total score of 5 out of 10 is required, and for each written exam it is furthermore required to obtain a grade 4 (out of 10).

Students not passing these requirements will take a recovery exam. This subject does not contemplate the single evaluation system.

Bibliography

  1. Delgado, R. Probabilidad y estadística para ciencias e ingenierías. Delta Publicaciones, 2007. 
  2. Bardina, X., Farré, M. Estadística descriptiva, Manuals UAB, 2009.
  3. Devore, Jay L. Probabilidad y Estadística para ingeniería y ciencias, International Thomson Editores, 1998.

Software

Microsoft Excel

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 1 Catalan first semester morning-mixed
(PAUL) Classroom practices 1 Catalan first semester morning-mixed
(SEM) Seminars 1 Catalan first semester morning-mixed
(PAUL) Classroom practices 2 Catalan first semester morning-mixed
(SEM) Seminars 2 Catalan first semester morning-mixed
(SEM) Seminars 3 Catalan first semester morning-mixed
(SEM) Seminars 4 Catalan first semester morning-mixed