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Analysis of Statistical Data
Scholar Year: 2018/2019 - 1S
| Code: |
LGRHP1116 |
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Acronym: |
AD |
| Scientific Fields: |
Métodos Quantitativos |
Courses
| Acronym |
Nº of students |
Study Plan |
Curricular year |
ECTS |
Contact hours |
Total Time |
| LGRHPL |
60 |
Study Plain |
2º |
5,5 |
60 |
148,5 |
Teaching language
Portuguese
Intended learning outcomes (Knowledges, skills and competencies to be developed by the students)
At a time when the development of computer science is advancing rapidly, the analysis of statistical data has assumed a key role in the field of modeling, prediction and interpretation of phenomena in areas such economics, finances, marketing and management.
In the curricular unit of Analysis of Statistical Data our aim is to present the major statistical methods used for analyzing data in social and business sciences; correlation analysis; simple and multiple linear regression; principal component analysis and factor analysis. All these statistical tools will allow students to interpret, formalize and solve relevant problems involving a quantitative data analysis.
This course will show our students that the statistical theoretical foundations are essential but our classes will be eminently practical using statistical software such as SPSS to solve practical problems using, whenever possible, real data.
Syllabus
Basic statistical concepts and introduction to the software SPSS
Parametric Tests
t test for one sample
t test for two independent samples
t test for two paired samples
ANOVA
Levene's test
Applications with SPSS
Non Parametric Tests
Kolmogorov-Smirnov / Shapiro-Wilk test
Mann-Whiteney test
Kruskal-Wallis test
Applications with SPSS
Simple and Multiple Linear Regression
Correlation Analysis (Spearman vs Perason)
Model definition and assumptions
Parameters Estimation
Statistical Inference
Multicollinearity
Heteroscedasticity
Autocorrelation
Applications with SPSS
Factorial Methods
Analysis in Principal Components
Factor Analysis
Reliability Analysis - Cronbach's Alpha
Applications with SPSS
Software
SPSS
Demonstration of the syllabus coherence with the UC intended learning outcomes
The contents of this curricular unit are built to allow the acquisition of the theoretical background needed in several areas of business and management and the consequent practical application of them. The program begins with a brief review of basic statistical concepts, arming students with the basic statistical tools essential to the learning of other content. At the same time an introduction to the appropriate software is made. Then the most important concepts in several areas of multivariate statistical analysis and forecasting methods are introduced. The acquisition of new knowledge in parallel with the use of statistical software allows students to achieve the defined objectives: to formalize, to interpret and to solve real problems with real data.
Teaching methodologies
The teaching methods applied are defined according to the type of classes (theoretical and laboratory) depending also on the type of objective.
Theoretical Classes: Expositive / Interrogative Methodology, making use of participatory methodology, whenever possible;
Laboratory Classes: Participatory Methodology through the realizations of exercises, using a statistical software;
Student attendance: Clarification of doubts; support to the student’s study; support for carrying out students practical work.
Demonstration of the teaching methodologies coherence with the curricular unit's intended learning outcomes
The selection of methods to be used is based on defined objectives.
In the curricular unit of Analysis of Statistical Data lectures are given to a large number of students simultaneously and the educational objectives aimed predominantly a cognitive knowledge.
This theoretical classes are mainly based on expository methods but also supported by practical examples and, whenever possible, encouraging students participation. This method used in the theoretical classes with the invitation to participate, helps to clarify concepts, helps to reflect on the contents and help students in structuring, discrimination and integration of cognitive elements, developing the critical thinking and the mathematical reasoning.
The laboratory classes are focused on the idea of “know-how”, supported on practical activities of solving exercises and problems through the application of concepts provided in the theoretical classes using the appropriate software. These activities should be performed mainly by the students; the teacher should only facilitate.
Theoretical classes are followed by sequential practical laboratorial classes with exercises in order to apply all the knowledge learned in previous theoretical classes. These sequential exercises help and reinforce the knowledge and to understand that the theoretical knowledge is essential to a good practical application of it.
Assessment methodologies and evidences
The Continuous Evaluation comprises two components:
• Individual Component (CI) composed by two mini-teste and a final test;
• Group Component (CG) composed by a group work.
Designating
MTF = 0,15 * 1ºmini-test +0,15 * 2ºmini-test + 0,7 * Final Test
(rounded to tenths) of the weighted average of the two mini-tests and the final test.
The NOTE OF THE INDIVIDUAL COMPONENT (CI) corresponds to the maximum between MTF and the Final Test grade, rounded to the tenths.
The GROUP COMPONENT GRADE (CG) consists of the evaluation of the written report and the evaluation of the oral presentation, obtained as follows:
CG = 0,7 * Written report grade +0,3 * Oral presentation grade
The FINAL GRADE is:
Final Grade= 50%(CI) + 50%(CG)
The mini-tests do not have a minimum grade; the final test has as minimum grade 7,5 values ; obviously the Individual Component has a minimum grade 10 values; the group work report has a minimum grade of 10 values; obviously the Group Component also has a minimum grade of 10 values; if this grade is not reached in any of the components, or if the final grade, rounded to the units, is less than 10 values, the student will have to make a final evaluation.
If the student misses or gives up any of the evaluation moments will be excluded from the continuous evaluation.
The group work will have to comply a schedule of activities defined by the teacher, which include a report, a oral presentation and discussion.
The groups should consist of 2 to 4 students.
Note: The option between Continuous Evaluation and Normal Season is mandatory. The student will have to make this option through the MOODLE platform until one week before the first mini-test. If the student chooses continuous evaluation and is absent, the student is not allowed to take the normal period exam.
Final Evaluation
There are three seasons of final evaluation:
Normal Season | 1st season (intended for students who did not option for continuous assessment)
The Normal Season is composed by:
• CG - Group Component
• E - Individual Exame
The group work will have to comply a schedule of activities defined by the teacher, which include a report, a oral presentation and discussion.
The groups should consist of 2 to 4 students.
The minimum grade required for either the group work report (and consequently for the Group Component) or for the exam is 10 values. The exam and work notes are rounded to the tenths.
The GROUP COMPONENT GRADE (CG) consists of the evaluation of the written report and the evaluation of the oral presentation, obtained as follows:
CG = 0,7 * Written report grade +0,3 * Oral presentation grade
Final Grade = 50%(E) + 50%(CG)
If the Final Grade, rounded to the units, is less than 10 values, there will be no approval.
The 2nd season (it is intended for students who did not or did not obtain use in the normal season or in the continuous evaluation).
The evaluation system is the same as in the final evaluation of the Normal Season.
Bibliography
MARTINEZ, L. F. e FERREIRA, A.J.;Análise de dados com o SPSS – Primeiros Passos, 2008. ISBN: Escolar Editora
CHAVES, C., MACIEL, E., GUIMARÃES, P. e RIBEIRO, J.C.;Instrumentos Estatísticos de apoio à Economia: conceitos básicos, McGraw-Hill, 2000
MAKRIDAKIS, S., WHEELWRIGHT, S. e HYNDMAN, R.;Forecasting: Methods and Applications, John Wiley & Sons, New York, 1998
PESTANA, M. A. e GAGEIRO, J. N.;Análise de dados para Ciências Sociais – A Complementaridade do SPSS, Edições Sílabo, 2008
Complementary Bibliography
REIS, E;Estatística Multivariada Aplicada, Edições Sílabo, Lisboa, 1997
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