Checking date: 08/06/2021

Course: 2021/2022

Exploratory data analysis
(13696)
Study: Bachelor in Statistics and Business (203)

Coordinating teacher: AUSIN OLIVERA, MARIA CONCEPCION

Department assigned to the subject: Department of Statistics

Type: Basic Core
ECTS Credits: 6.0 ECTS

Course:
Semester:

Branch of knowledge: Social Sciences and Law

Objectives
The aim of this course is that students learn how to organize, represent, analyze and summarize the information contained in a dataset by the use of the appropriate graphical, tabular and numerical methods according to the type of data and variables observed. SPECIFIC COMPETENCES 1. Distinguish different types of variables and data. 2. Synthesize tabular, numeric and graphical statistical information. 3. Propose and validate the simple linear regression model as a model for the relationship between two continuous variables. TRANSVERSAL COMPETENCES: 1. Capacity of analysis and synthesis of information. 2. Setting up and solving practical problems. 3. Written and verbal communication.
Description of contents: programme
1. Introduction 1.1. What is Statistics. Definition. 1.2. General concepts. 1.3. Sample methods. 2. Descriptive statistics for a single variable. 2.1 Frequency distribution. Grouping by classes. 2.2. Frequency distribution. Grouping by class intervals. 2.3. Graphical displays. 2.4. Numerical measures for a univariate distribution. 3. Transformations. 3.1. Linear transformations. 3.2. Non linear transformations. 4. Joint description of various variables. 4.1. Two-way tables. Joint frequency distribution. 4.2. Graphical displays. 4.3. Marginal frequency distributions. Conditional frequency distributions. 4.4. Numerical measures for linear association. Pearson's correlation coefficient. 4.5. Spearman's correlation coefficient. 4.6. Association measures for contingency tables. 5. Relations between variables. 5.1. Simple linear regression. The least squares method.
Learning activities and methodology
Theory classes with support material available on the web, problem solving classes, practical classes with statistical computing packages in computing labs.
Assessment System
• % end-of-term-examination 60
• % of continuous assessment (assigments, laboratory, practicals...) 40
Basic Bibliography
• A. Agresti. Categorical Data Analysis. Wiley. 2002
• J. Tukey. Exploratory Data Analysis. Addison-Wesley. 1977

The course syllabus may change due academic events or other reasons.