Teaching plan for the course unit

 

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General information

 

Course unit name: AnÓlisi Quantitativa Aplicada a l'Empresa Internacional

Course unit code: 573238

Academic year: 2018-2019

Coordinator: Javier Manuel Romani Fernandez

Department: Faculty of Economics and Business

Credits: 2,5

Single program: S

 

 

Estimated learning time

Total number of hours 62.5

 

Face-to-face learning activities

26

 

-  Lecture with practical component

 

19.5

 

-  Problem-solving class

 

6.5

Supervised project

18

Independent learning

18.5

 

 

Competences to be gained during study

 

CB7 Capacity to apply the acquired knowledge to problem-solving in new or relatively unknown environments within broader (or multidisciplinary) contexts related to the field of study.

 

CB8 Capacity to integrate knowledge and tackle the complexity of formulating judgments based on incomplete or limited information, taking due consideration of the social and ethical responsibilities involved in applying knowledge and making judgments.

 

CB9 Capacity to communicate conclusions, judgments and the grounds on which they have been reached to specialist and non-specialist audiences in a clear and unambiguous manner.

 

CB10  Skills to enable lifelong self-directed and independent learning.

 

CE5 Understanding of and capacity to apply marketing tools to solve problems and generate opportunities and to use theoretical expertise and the appropriate research tools to solve problems encountered in the fields of market research and international marketing.

 

CE6 Understanding of the heuristics, methodologies and techniques required for decision-making in the field of international business operations.

 

CE10 Capacity to acquire an advanced level of competence in the writing of scientific documents, specialized reports and research papers in which value judgements are formulated, complying with standard criteria for publication or for presentation to potential stakeholders or other interested parties at the global level.

 

 

 

 

Learning objectives

 

Referring to knowledge

To distinguish among different types of quantitative data (categorical, continue, etc.) and recognize the types of information they provide, as well as their limitations

 

To recognize the main types of distribution, how they interact with available data and how could they be used in the statistical analysis

 

To infer about the characteristics of the population based on the samples

 

To recognize the key characteristics of the statistical tests (significance, power, confidence intervals) and to realize statistical tests on data

 

To understand the concepts of correlation, partial correlation, simple and multivariate regression

 

To generate and interpret the results obtained using the existing software

 

To discuss and recommend solutions to problems detected in the analysis of an specific phenomenon

 

To inform and interpret the results of the analysis in a clean and effective way for a reader with no technical knowledge on statistics and econometrics

 

To make recommendations based on the results of the analysis

 

 

Teaching blocks

 

1. 1. The concept and content of statistics

*  1.1. The goal of statistics
1.2. Descriptive statistics and statistical inference
1.3. Population and samples
1.4. Data; Classification and scales of measurement
1.5. Statistical sources

2. 2. One-dimensional frequency distribution and graphic representation

*  2.1. Frequency distributions
2.2. Graphic representations
2.3. Exploratory data analysis: Stem-and-leaf plot

3. 3. Measures of position

*  3.1. Arithmetic mean, median and mode
3.2. Properties
3.3. Measures of location: Quantiles

4. 4. Measures of dispersion and shape

*  4.1. Dispersion: Box plots
4.2. Variance and standard deviation
4.3. Linear transformations: Standardized variables
4.4. Measures of shape

5.
5. Two-dimensional frequency distribution

*  5.1. Joint frequency distributions; Marginal distribution
5.2. Conditional distribution
5.3. Statistical independence
5.4. Contingency tables: Association between attributes

6. 6. Association between variables

*  6.1. Scatter plots
6.2. Linear association: Covariance
6.3. Pearson’s correlation coefficient
6.4. Linear regression

7. 7. Introduction to probability theory

*  7.1. Random experimentation; Probability: Axiomatic and properties
7.2. Conditional probability
7.3. Intersection theorem: Independence of events
7.4. Total probability theorem; Bayes’ theorem

8. 8. One-dimensional random variables

*  8.1. Random variable: Discrete, continuous
8.2. Probability distribution: Quantity function and density function
8.3. Distribution function
8.4. Mathematical expectation and variance; Standardized variable

9. 9. One-dimensional probability distribution

*  9.1. Dichotomous and binomial distribution
9.2. Normal distribution

10. 10. Distribution models for random variables

*  10.1. Distribution models for discrete and continuous random variables
10.2. Distributions derived from normal distribution
10.3. Convergence: Central limit theorem

11. 11. Elements of sampling theory

*  11.1. Basic concepts: Random and statistical samples
11.2. Distribution of some statistics in a sample

12. 12. Point estimation

*  12.1. Introduction to the estimation process
12.2. Properties of point estimators
12.3. Methods of point estimation

13. 13. Interval estimation

*  13.1. Definition of confidence intervals
13.2. Confidence intervals for sample means and mean differences
13.3. Confidence intervals for proportions and proportion differences
13.4. Confidence intervals for variance
13.5. Choosing sample size

14. 14. Contrasts of statistical hypotheses

*  14.1. Basic concepts: Critical regions
14.2. Types of errors; The power of a contrast
14.3. Means testing and equality of means
14.4. Variance testing and equality of variance
14.5. Proportions testing and equality of proportions

15. 15. Chi-square tests

*  15.1. Goodness of fit test
15.2. Test of independence

 

 

Teaching methods and general organization

 

The teacher exposes in class the basics of each subject, and provides the student with material so that he can carry out autonomous learning. In the classroom, the key aspects are discussed later, so that a participatory attitude of the students is encouraged. In addition, some practical lessons will take place in the computer room using software for data analysis, in order to learn the practical implementation of the theoretical concepts, analyze the results and understand them.

 

 

Official assessment of learning outcomes

 

Continuous assessment consists of:

 

— The first consists of a set of tasks that are worth 40% of the final grade and which the teacher details at the beginning of the course. As an example, students can choose a topic of interest to investigate in the business field and develop the work as the theoretical contents necessary for every stage are developed in the classroom. This process is always carried out by providing the necessary feedback between the teacher and students. Finally, the student obtains a database that can support the application of the statistical techniques that are presented in the last lessons of the course.
 

—A final examination set by the Academic Committee in the standard academic calendar, which accounts for the remaining 60% of the final grade. The examination contains multiple-choice questions on the theoretical and applied content of the course.

 
For the marks for assessment tasks and the examination to be considered in the calculation of the final grade, students must complete a minimum of 80% of class exercises and obtain a mark of at least 3.5 in the final examination; otherwise, they are automatically entered for single assessment.

 

Examination-based assessment

Examination-based assessment

Students who do not wish to be assessed on a continuous basis are entered for single assessment, which consists of an end-of-semester examination worth 100% of the final grade for the subject. The examination date is set by the Academic Council. This examination consists of several questions on the theoretical and practical aspects of the course content.

 

 

Reading and study resources

Consulteu la disponibilitat a CERCABIB

Book

Paul Newbold, William L. Carlson, Betty M. Thorne: Statistics for business and economics. 8th Edition. Pearson Education, 2013.

Douglas A. Lind, William G. Marchal, Samuel A.Wathen: Basic statistics for business and economics, 8th Edition. McGraw-Hill, 2013.

Daniel Peña: Fundamentos de Estadística. Alianza Editorial, 2014