Bachelor in data and business analytics (Plan: 560 - Estudio: 203)
Coordinating teacher: MARIN DIAZARAQUE, JUAN MIGUEL
Department assigned to the subject: Statistics Department
Type: Electives
ECTS Credits: 6.0 ECTS
Course: 4º
Semester:
Requirements (Subjects that are assumed to be known)
Probability
Statistical Inference I
Statistical Inference II
Objectives
General objectives:
1. Capacity for analysis and synthesis.
2. To model and solve problems.
3. Oral and written communication skills.
Specific objectives:
1. Knowledge, both in theory and practice, of the foundations of the main techniques of survey sampling.
2. Differentiation of the different types of sampling.
3. Ability to make inference in finite populations under complex sampling designs.
Description of contents: programme
1. Introduction and general considerations about sampling techniques and Official Statistics
- 1.1. Importance of Official Statistics and its role in Society
- 1.2. Sampling Basics
- 1.3. Types of sampling: probabilistic vs. non-probabilistic
- 1.4. Introduction to the statistical software R and its importance in Official Statistics
2. Probability Sampling
- 2.1. Definition and principles of probability sampling
- 2.2. Advantages and limitations of probability sampling
- 23. Implementation in R: basic functions for probability sampling
3. Simple random sampling
- 3.1. Concepts and theory of simple random sampling
- 3.2. Procedures for performing simple random sampling
- 3.3. Examples of simple random sampling in different contexts
- 3.4. Using R to perform simple random sampling: specific functions and packages
4. Stratified random sampling
- 4.1. Fundamentals of Stratified Random Sampling
- 4.2. Criteria for stratification and selection of strata
- 4.3. Advantages of stratified sampling over other techniques
- 4.4. Running Stratified Sampling in R: Recommended Code and Packages
5. Ratio and Regression Estimators
- 5.1. Introduction to ratio and regression estimators
- 5.2. Applications of ratio and regression estimators in research
- 5.3. How to calculate ratio and regression estimators in R
- 5.4. Practical examples: use of estimators for data analysis
6. Cluster Sampling
- 6.1. Basic concepts of cluster sampling
- 6.2. Design and analysis of cluster samples
- 6.3. Comparison with other sampling techniques
- 6.4. Implementation of cluster sampling in R
Learning activities and methodology
Competences will be acquired by students both through theoretical lectures and the resolution of assigned homeworks.
There will also be practical classes of exercises.
Assessment System
% end-of-term-examination/test 40
% of continuous assessment (assigments, laboratory, practicals...) 60
