COURSE GUIDE Universidad Católica de Valencia. San Vicente Mártir. Second Year. Descriptive Statistics

Transcription

1 1 COURSE GUIDE Universidad Católica de Valencia San Vicente Mártir Second Year Descriptive Statistics 2018/2019

2 2 Teaching guide: Descriptive Statistics COURSE GUIDE TO Descriptive Statistics ECTS MODULE: Quantitative Methods 36 Subject: Statistics 6 Coruse: Descriptive statistics 6 Type of learning: Basic training Teacher: Dr. Maria Escrivà Year: second Semester: first Department: Economics, management and marketing maria.escriva@ucv.com SUBJECT ORGANIZATION Quantitative Methods ECTS 36 Duration and temporal location within the curriculum: The module includes 4 general subjects and 6 courses, being the subjects of Mathematics for Economics and Business and Descriptive Statistics basic character, and Information Systems for Management, Statistical Inference and Econometrics mandatory, covering all degree courses, from 1st to 4th. Descriptive statistics is taught in the second degree course in Business Administration and is the first contact with the statistics. With this module, students achieve the homogenization of knowledge in basic science to further promote the transfer of credits between qualifications. Subjects and Courses Subject Information Systems Courses ECT S Course/ semester Information Systems Management I 6 1/1 Information Systems Management II 6 2/2 Mathematics Business mathematics 6 1/2 Statistics Descriptive statistics 6 2/1 Statistical Inference 6 3/2

3 3 Statistical and econometric methods Econometrics 6 4/1 COURSE GUIDE TO THE SUBJECT: Descriptive statistics Prerequisites: none GENERAL GOALS a. Knowing the tools of descriptive statistics, its usefulness, limitations and interpretation. b. Develop critical skills of students when they make a decision based on available data or generated by a random experiment. c. Accustom the students to use informatics (spreadsheet or statistics applications) for describing and analyzing data. This course studies the three basic tools of descriptive statistics: tables, graphs and statistical parameters that will be useful to transform data sets into useful information for decision making. We also study the probability and the probability distribution models, with particular emphasis on the standard model. The course is given without assuming prior knowledge of statistics or probability, although it is assumed that students are familiar with using spreadsheet, primarily through the disciplines of Computing from the previous years. CROSS-SECTIONAL COMPETENCES Competence measuring scale Systemic CG1 Capacity for analysis and synthesis X CG2 Capacity for time and resources management CG3 Capacity for applying knowledge in practice X CG4 Ability to retrieve information from different sources X CG5 Oral and written communication X CG6 Elementary computing and IT skills X CG7 Information management skills

4 4 Teaching guide: Descriptive Statistics Instrumental CG8 Problem solving X CG9 CG10 Decision-making Basic knowledge of a second language CG11 Creativity and capacity for generating new ideas X CG12 Initiative and entrepreneurial spirit CG13 Capacity to learn and research skills X Interpersonal CG14 CG15 CG16 CG17 Leadership Interpersonal skills Self-Confidence and decision-making under pressure Ability to work in an interdisciplinary team CG18 Ability to work autonomously X CG19 Ethical commitment CG20 Development of values related to the principles of equal opportunities between men and women, universal accessibility for disabled people and, in general, the democratic values and those of a culture of peace SPECIFIC COMPETENCES Disciplinary Identifying the impact of macro- and micro economic CE1 elements on business organizations (i.e. financial and monetary systems, internal markets) Identifying the constitutional characteristics of an CE2 organization (i.e. goals and objectives, ownership, size, culture) Identifying the functional areas of an organization and their CE3 relations (i.e. purchasing, logistics, marketing, finance, human resources)

5 5 CE4 CE5 CE6 Ability to develop interdisciplinary knowledge and analysis to define criteria according to which an enterprise is defined, linking the results with the analysis of its environment Understand existing and new technology and its impact on new / future markets Change management in an organization Professional Capacity to manage a company or organization, CE7 understanding its competitive and institutional positioning and identifying its strengths and weaknesses. Managing a company through planning and controlling by CE8 using concepts, methods and tools (i.e. strategy design and implementation, benchmarking, TQM, ABC Costing, etc) Identifying the potential sources of useful economic CE9 information and their content. CE10 CE11 Identifying and using management software properly Design and implement information systems in the company CE12 Understanding the principles of law and link them with business/management knowledge CE13 Ability to audit the situation and foreseeable evolution of a company, using the correct information CE14 Capacity to issue reports advising on specific situations of companies and markets. Attitudinal CE15 Capacity to obtain from data, valuable information useful for decision-making X CE16 Technical understanding, reading, speaking and writing in a foreign language, especially in English CE17 CE18 CE19 Applying professional criteria to analyze business problems Capacity for integration in any functional area of a company or organization, for performing any management task Empathy and capacity to understand other people CE20 Ability for negotiation and the resolution of conflicts

6 6 Teaching guide: Descriptive Statistics LEARNING OUTCOMES 1 R-1 Understand the tools of descriptive statistics (tables, graphs and statistics) and know where to apply in every case. R-2 Can understand and develop a descriptive study of a random variable. R-3 Can understand, quantify and express the linear relationship between two numerical variables. R-4 Understand the basic principles of probability theory and can apply them to solve simple problems. R-5 Understands and applies basic concepts of random variable and probability distribution. Knows the main discrete distributions (Binomial, Poisson and geometric) and continuous (Uniform, Exponential and Normal). COMPETENCES CG: 1, 3, 4, 6, 13, 18 CG: 1, 3, 4, 5, 6 CG: 1, 3, 4, 6, 8, 13 CG: 1, 3, 4, 5, 6, 8, 11, 13, 18 CG: 1, 3, 4, 6, 13, 18 1 List sequentially learning outcomes f ollowing the nomenclature proposed Important Notice: Competences are expressed in a generic sense but they get concretion in learning outcomes. These outcomes constitute a realization of one or more skills, making explicit the level of performance acquired by the student that will be evaluated. Learning outcomes demonstrate what the student will be able to show at the end of the course or subject and reflect also the degree of acquisition of competence or skill set.

7 7 TRAINING ACTIVITIES (CLASSROOM ACTIVITIES) ACTIVITY METHODOLOGY Relationship with Learning Outcomes ECTS 2 THEORETYCAL LECTURES Class sessions will involve lectures, video shows, and presentations of related topics and current issues related to course contents R-1, R-2, R-3, R-4, R- 5 1,5 PRACTICAL SESSIONS Practical classes will involve group activities including case studies, debates, and management games. Students should pay attention to the class schedule as lectures may be held in a classroom, open area or even in an organization. R-1, R-2, R-3, R-4, R- 5 0,5 ACADEMIC TUTORIALS Personalized attention in small groups. A period of instruction and / or guidance by a tutor in order to review and discuss the materials and topics presented in lectures, seminars, readings, performance of works, etc. R-1, R-2, R-3, R-4, R- 5 0,25 MAKING EXAMS Set of oral and / or written examinations during the term R-1, R-2, R-3, R-4, R- 5 0,15 Total (2,4*) 2 The subject and / or material is organized in CLASSROOM ACTIVITIES AND SELF-CONDUCTED INDIVIDUAL OR GROUP ACTIVITIES, with an estimation in ECTS. An appropriate distribution is as follows: 35-40% for classroom training activities and 65-60% for self-conducted activities. (For a course of 6 ECTS: 2.4 and 3.6 respectively). The learning methodology is described in this guide in generic terms, which is specified in the teaching units that constitute each subject and / or matter

8 8 Teaching guide: Descriptive Statistics STUDENTS PREPARATORY ACTIVITIES ACTIVITY Methodology Relationship with Learning Outcomes ECTS INDIVIDUAL STUDENT S WORK Individual preparation of readings, essays, problem solving, seminars, papers, reports, practical classes and / or small group tutoring. Work done through the intranet ( ) R-1, R-2, R-3 3,6 Total (3,6*)

9 9 ASSESSMENT SYSTEM (On Campus students) Assessment Instrument 3 LEARNING OUTCOMES Percentage Class attendance and participation R1, R2, R3, R4, R5 10 Homework R1, R2, R3, R4, R5 10 Midterm test exams R1, R2, R3, R4, R5 20 Midterm Exercises R1, R2, R3, R4, R5 10 Final Exam R1, R2, R3, R4, R5 50 Honor s criteria: Students with exceptional performance, if any, will be awarded with the maximum grade, according to the teacher s assessment, with a limitation of one honor per 20 students o a fraction of 20. In order to pass the course, it will be mandatory to have submitted and pass the proposed activities throughout the course. In any case, the students will have to pass the final written exam. 3 Assessment tools and techniques: review-oral, written tests (multiple choice tests, developmental, conceptual maps...), tutorials, projects, case studies, observation notebooks, portfolio, etc.

10 10 Teaching guide: Descriptive Statistics CONTENTS DESCRIPTION COMPETENCES 1.- Data Description: frequencies and its representation 2.- Description of data: numerical measures 3.- Data Description: presentation and analysis 4.- Probability concepts 5.- Discrete probability distributions 6.- Continuous probability distributions CG: 1, 3, 4, 5, 6, 8, 11, 13, 18 CG: 1, 3, 4, 5, 6, 8, 11, 13, 18 CG: 1, 3, 4, 5, 6, 8, 11, 13, 18 CG: 1, 3, 4, 5, 6, 8, 11, 13, 18 CG: 1, 3, 4, 5, 6, 8, 11, 13, 18 CG: 1, 3, 4, 5, 6, 8, 11, 13, 18

11 11 BIBLIOGRAPHY Class lectures and materials provided by the professor BASIC BIBLIOGRAPHY: Lind, D. A., Marchal, W. G., & Wathen, S. A. (2016). Statistical techniques in business & economics (Vol. 11). New York, NY: McGraw-Hill ADDITIONAL BIBLIOGRAPHY: Gujarati, D. N., & Porter, D. (2009). Basic Econometrics Mc Graw-Hill International Edition. González, G. M. (2007). Introducción a la estadística. Universidad Católica de Valencia. Berenson, M. L., Levine, D. M., & Krehbiel, T. C. (2014). Estadística para administración. Pearson Education. Parra Frutos, I. (2003). Estadística empresarial con Microsoft Excel: Problemas de inferencia. AC Libros científicos y técnicos Montiel Torres, A. M., Barón López, F. J., & Rius Díaz, F. (1997). Elementos básicos de estadística económica y empresarial. PRENTICE HALL

12 12 Teaching guide: Descriptive Statistics WORK PLANNING: LESSON NUMBER OF LECTURES 1 Data Description: frequencies and its representation 6 2 Description of data: numerical measures 7 3 Data Description: presentation and analysis 6 4 Probability concepts 6 5 Discrete probability distributions 6 6 Continuous probability distributions 8 WORK PLANNING FOR SECOND AND FURTHER ENROLLMENTS: There will be a special group for those students who have not registered for the first time, and a teacher responsible of this group. This teacher has to schedule six two-hour sessions for monitoring and mentoring. In each session the subject will be developed so as to reinforce the work of the skills that each student needs to pass the course. The assessment contained in the examination will be established in the official calendar of this subject. These sessions are available on the specific schedule. The blocks of content and tasks to be performed in each session are as follows: WORK PLANNING FOR SECOND AND FURTHER ENROLLMENTS LESSON NUMBER OF LECTURES 1 Data Description: frequencies and its representation 1 2 Description of data: numerical measures 1 3 Data Description: presentation and analysis 1 4 Probability concepts 5 Discrete probability distributions 1,5

13 13 6 Continuous probability distributions 1,5