Universitat Internacional de Catalunya
Descriptive Data Analysis
Other languages of instruction: English, Spanish
If the student is enrolled for the English track then classes for that subject will be taught in the same language.
Teaching staff
Lecture days or else by appointment
Introduction
A course in basic statistics is offered in a wide variety of disciplines, from the social sciences to business to the natural sciences. The same statistical methods are applied across disciplines. Therefore, it should not be surprising that the tools you will learn to use in this course will benefit you in your future studies and career, regardless of whether your career interest is finance, accounting, strategy, management or marketing. In this course you will learn basic statistical measures, descriptive statistical methods, sampling methodology and the main probability distributions.
I believe statistics is best taught through a series of clear and carefully worked examples. A theoretical background to descriptive and inferential statistical methods will be provided, however much of the time will be spent teaching you how to apply the theory to the real world. Statistics is not about memorising formulas: it is about recognising the appropriate statistical test to perform in a given situation. This requires practice by the student. As we cover the topics, if you do not have a clear understanding of one topic it is wise to seek help immediately. The next topic will build upon the previous one. Please allow me to assist you as soon as you find that you have any questions.
Pre-course requirements
Before taking this module, it is highly recommended that students have completed Mathematics 1, Mathematics 2 and Information Systems.
Objectives
To learn the terminology, notation and different methods of quantitative analysis.
To be able to identify and understand the fundamental concepts of quantitative analysis.
To be able to analyse and synthesise information presented in the classroom and complementary material provided by the lecturer.
To be able to select appropriate statistical or mathematical methods for solving a particular economic problem.
Competences/Learning outcomes of the degree programme
- 19 - To analyse quantitative financial variables and take them into account when making decisions.
- 28 - To be able to work in another language and use terminology and structures related to the economic-business world.
- 31 - To develop the ability to identify and interpret numerical data.
- 32 - To acquire problem solving skills based on quantitative and qualitative information.
- 35 - To analyse time series.
- 36 - To interpret quantitative and qualitative data and apply mathematical and statistical tools to business processes.
- 40 - To be able to choose statistical methods appropriate to the object of analysis.
- 41 - To be able to descriptively summarise information.
- 42 - To be able to empirically analyse financial phenomena.
- 43 - To acquire skills for using statistical software.
- 50 - To acquire the ability to relate concepts, analyse and synthesise.
- 51 - To develop decision making skills.
- 52 - To develop interpersonal skills and the ability to work as part of a team.
- 53 - To acquire the skills necessary to learn autonomously.
- 54 - To be able to express one’s ideas and formulate arguments in a logical and coherent way, both verbally and in writing.
- 56 - To be able to create arguments which are conducive to critical and self-critical thinking.
- 64 - To be able to plan and organise one's work.
- 65 - To acquire the ability to put knowledge into practice.
- 66 - To be able to retrieve and manage information.
- 67 - To be able to express oneself in other languages.
Learning outcomes of the subject
To learn the terminology, notation and different methods of quantitative analysis.
To be able to identify and understand the fundamental concepts of quantitative analysis.
To be able to analyze and synthesize information presented in the classroom and complemetary material provided by the teacher.
To be able to select appropriate statistical or mathematical method to solve a particular economic problem.
Syllabus
Lesson 1.Descriptive statistics: concepts, measures, graphs. What is statistics? Descriptive versus inferential statistics. Sample and population. Types of data. Measurements of central tendency. Measurements of dispersion. Measurements of shape. Measurements of concentration. Bar charts and pie charts. Frequency distribution. Other type of graphs. Misleading graphs.
Lesson 2. Bivariate analysis: frequencies, tables, scatter plots, conditional and marginal distribution, measures, etc. Covariance and correlation.
Lesson 3.Probability and distribution functions. Introduction to probability. Classical probability. Tree diagrams. Bayes' theorem. Moments about the origin. Central moments. Discrete versus continuous probability distribution.
Teaching and learning activities
In person
Theoretical explanations will be presented in the classroom on PowerPoint slides, accompanied by additional explanations on the board.
The theory will be combined with problem-solving.
Problems will be solved jointly between the lecturer and students as a way of improving the learning process.
Evaluation systems and criteria
In person
Two evaluation methods will be used:
1. Class activities and participation (20%)
2. Final examination (80%)
If a midterm is carried out during the term then percentages will be 30/70. The grade of the final exam must be above 4/10.
If any student does not pass the course at the first attempt and is required to retake in July, the final grade will be that of the second-sitting examination.
Bibliography and resources
Students’ proficiency in Statistics 1 will be achieved by means of active practice. This means working on problems and understanding and explaining the results. The various textbooks recommended have hundreds of problems students can use to gain additional practice. Answers to most of the problems can be found at the back of the textbooks.
Keller, G. Statistics for Management and Economics. South Western Cengage Learning.
Lind, D.A., Marchal, W.G. & S.A. Wathen. Statistical Techniques in Business and Economics. McGraw-Hill International Edition.
Schiller, John J. & Srinivasan, R. Alu. Schaum's Outline of Probability and Statistics. Ringgold, Inc.
Wonnacott T. & Wonnacott R.J. Introductory Statistics. John Wiley & Sons.
Notice that additional material may be handed over in class or shared through the virtual classroom.