Checking date: 21/04/2019


Course: 2019/2020

Data analysis in marketing
(16226)
Study: Master in Marketing (269)
EPE


Coordinating teacher: KAISER REMIRO, REGINA

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:




Students are expected to have completed
Not required.
Competences and skills that will be acquired and learning results.
Skills to be acquired General skills *CG2: Effective knowledge of other disciplines / techniques used in Marketing and Market Research. *CG6: Ability to search and analyze information from different sources. Specific skills: *CE4: To learn the qualitative and quantitative tools for market research, to choose and apply the most appropriate technique to every problem, and understand the potential of computer tools in this area. *CE5: To understand and use statistics and econometrics tools to analyze data and marketing problems through scientific models, using appropriate software. Learning Objectives ¿ Understand the foundations of Statistics. ¿ Sampling from finite populations ¿ Apply fundamental principles and methods of Statistics to a wide range of problems. ¿ Design, correctly implement and document solutions to the "real-world" problems.
Description of contents: programme
Chapter 1. Descriptive statistics for marketing analysis. 1.1 Introduction. 1.2 Types of marketing data. 1.3 Scalar measures. 1.4 Graphical displays. Sampling. SPSS examples. Chapter 2. Inferential statistics. 2.1 Basic foundation of inferential statistics. 2.2 Point and interval estimation of population parameters. 2.3 Testing of hypotheses about population parameters. 2.4 Hypotheses about the differences among two populations. SPSS examples. Chapter 3. Associative statistics. 3.1 Concept of association among two variables. 3.2 Types of relationships. 3.3 Cross tabulations and chi-square analysis. 3.4 Correlation. SPSS examples. Chapter 4. Predictive statistics. 4.1 Basic concept of prediction and regression analysis. 4.2 Bivariate and multiple linear regressions. 4.3 Other models. SPSS examples
Learning activities and methodology
Classes may involve lectures, small group exercises, case analyses and discussions. The lectures will serve to establish the conceptual foundations. Practical classes are designed so that students can develop skills and abilities required properly established. Student contributions are an important part of the course. Students are expected to read assigned materials for each class; attend class, participate and contribute to discussions.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Basic Bibliography
  • Devore, J.L., and N.R. Farnum. Applied Statistics for Enginners and Scientist, 2nd Edition. Duxbury Press, . 2004
  • Lind, D. Marchal, W.G. and Wathen, S. . Statistical Techniques in Business and Economics. 15th Edition.. Irwin/McGraw-Hill. 2011
  • Siegel, A.F.. Practical Business Statistics.. Academic Press.. 2011

The course syllabus and the academic weekly planning may change due academic events or other reasons.