Bachelor in Tourism (Study Plan 2017) (Plan: 381 - Estudio: 209)
Coordinating teacher: KAISER REMIRO, REGINA
Department assigned to the subject: Statistics Department
Type: Basic Core
ECTS Credits: 6.0 ECTS
Course: 1º
Semester: 2º
Branch of knowledge: Social Sciences and Law
Objectives
To acquire knowledge and understanding to
1. Analyze univariate and bivariate data
2. Analyze and interpretate relationships among two variables.
3. Knowledge and interpretation of the simple regression model.
4. Knowledge and interpretation of the multiple regression model.
5. Be able to solve problems using a statistical software.
1. Capacity for analysis and synthesis.
2. Knowledge of the use of statistical software.
3. Resolution of problems.
4. Teamwork.
5. Critical reasoning.
6. Oral and written communication.
Learning Outcomes
K4: Know the fundamentals of statistical analysis, the use of various sources of information and be able to process and analyse information to reach conclusions (about the market, society, etc.) useful for professional activity
Description of contents: programme
1. Introduction to exploratory analysis.
1.1. Importance on tourism.
1.2. Statistical terms: populations, subpopulations, individuals and samples.
1.3. Types of variables and search for sources of official tourism data.
2. Analysis of univariate data.
2.1. Representations and graphics of qualitative variables.
2.2. Representations and graphics of quantitative variables.
2.3. Time series graphics.
2.4. Dependence among qualitative data.
2.5. Parameter comparison for different populations.
2.6. Dependence among quantitative data.
3. Simple Linear Regression Model.
3.1. Model hypotheses.
3.2. Transformations.
3.3. Estimation and confidence intervals for the coefficients.
3.4. Individual significance and t-test.
3.5. R-square.
3.6. Prediction.
3.7. Diagnostics.
4. Multiple Linear Regression Model.
4.1. Model hypotheses.
4.2. Transformations.
4.3. Estimation and confidence intervals for the coefficients.
4.4. Individual significance and t-test.
4.5. Adjusted R-square.
4.6. Prediction.
4.7. Diagnostics.
5. Time series and index numbers.
5.1. Time series plot.
5.2. Components.
5.3. Index number. definition.
5.4. Simple and complex index numbers.
Learning activities and methodology
14 Theoretical support materials available on the Web, and 14 sessions based on problem-solving sessions and practical computing. No group tutorials except during the last week.
Assessment System
% end-of-term-examination/test 60
% of continuous assessment (assigments, laboratory, practicals...) 40
