General information
-
Academic year:
- 2025
-
Description:
- The main objective of this course is that students who are pursuing a doctorate at the UdG can begin to analyze, from a statistical point of view, a set of data and obtain conclusions supported by the presented methodology. Furthermore, students will acquire a statistical culture that will allow them to face an initial critical reading of scientific articles. All the concepts presented in the course will be treated in a practical way with specific data analysis software.
Introduction to statistical software. Data reading and operations with variables. Description of one-dimensional data. Two-dimensional data description. Association measures. Introduction to statistical inference. Estimation and contrasts. Fit in regression models. Linear regression and logistic regression.
-
Academic credits:
- 1
-
Course coordinator:
- Marc Saez Zafra
Groups
Other competences
- CG1- Select and systematize the information efficiently
CG2- Communicate orally and in writing on topics of their specialty in an original and creative way, adapting to the audience or the recipients (expert and non-expert audiences) and using the supports and / or resources that make them more effective oral and written productions
CE4- Use the appropriate sources of information to update knowledge on advances in research and on methods and proposals for intervention
CG4- Critically analyze the data and know how to interpret and extract significant results
CG7- Formally express the relationships between the variables involved in a problem, using the main computer, mathematical and statistical instruments to solve them
ECOCE4- Apply the basic tools of statistical inference and models.
Syllabus
1. - Descriptive Statistics
- Bivariate inference
- ANOVA and regression models
- Confounding and modification of the effect
Activities
|
Activity type
|
Hours with a teacher
|
Hours without a teacher
|
Virtual hours with a teacher
|
Total
|
| Participatory class |
30,00 |
0
|
10,00 |
40,00 |
| Hands-on class |
30,00 |
0
|
10,00 |
40,00 |
| Teamwork |
0
|
30,00 |
0
|
30,00 |
|
Total
|
60,00 |
30,00 |
20,00 |
110 |
Bibliography
Assessment and Grading
Assessment activities:
|
Description of the activity
|
Assessment Activity
|
% |
Remediable subject
|
| Participation in face-to-face sessions |
Participation in face-to-face sessions, proportional grade on a scale of 0-30. |
30 |
No |
| Level of participation and discussion in the face-to-face sessions |
Level of participation and discussion in the face-to-face sessions, proportional grade on a scale from 0 to 30. |
30 |
No |
| Presentation and correction of the final course work |
Presentation and correction of the final course work, proportional grade on a scale of 0 to 40. |
40 |
No |
Grading
Students will be graded according to the following parameters:
1. Participation in face-to-face sessions, proportional grade on a scale of 0-30.
2. Level of participation and discussion in the face-to-face sessions, proportional grade on a scale from 0 to 30
3. Presentation and correction of the final work of course, proportional note in a scale of 0 to 40.
The use of AI-based tools is permitted to perform exclusively these tasks:
- Information search.
- Improving the spelling, grammar and clarity of a text.
- Translating a text.
- Reviewing the answer.
In any case, it will be necessary to explain the following points:
- Cite the AI tools used in the preparation of the work.
- Specify the prompts that have been used.
- Specify the answer obtained
- Detail the process that has been followed to review and edit the answer.
Inappropriate use of AI, as well as failure to explain the required points, will be considered academic fraud.
Specific criteria for the "No show" grade:
Any of the following conditions:
Not having participated mostly in the face-to-face sessions.
Failure to submit the final course project.
Single Assessment:
There is no single assessment in this subject
Minimum requirements to pass:
To be considered to have passed the subject, a minimum grade of 5.0 must be obtained
Mentorship
e-mail to: marc.saez@udg.edu
Communication and interaction with students
e-mail to: marc.saez@udg.edu
Design Amendment
Amendment of activities:
In a part-time classroom setting due to COVID-19:
The virtual adaptation of the subject does not imply any change in the evaluation criteria and the weighting of the activities described in the original design.
In a fully virtual classroom setting due to COVID-19:
The approach is the same, by videoconference through one of the platforms recommended by the University.
Amendment of the assessment:
In a part-time classroom setting due to COVID-19:
The virtual adaptation of the subject does not imply any change in the evaluation criteria and the weighting of the activities described in the original design.
In a fully virtual classroom setting due to COVID-19:
The approach is the same, by videoconference through one of the platforms recommended by the University.
Mentoring and communication:
In a part-time classroom setting due to COVID-19:
The virtual adaptation of the subject does not imply any change in the evaluation criteria and the weighting of the activities described in the original design.
In a fully virtual classroom setting due to COVID-19:
The approach is the same, by videoconference through one of the platforms recommended by the University.