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ISCTE-IUL  >  Education  >  LFC , LGM

Data Analysis for Management (1 º Sem 2016/2017)

Code: L6043
Acronym: L6043
Level: 1st Cycle
Basic: No
Teaching Language(s): Portuguese, English
Friendly languages:
Be English-friendly or any other language-friendly means that UC is taught in a language but can either of the following conditions:
1. There are support materials in English / other language;
2. There are exercises, tests and exams in English / other language;
3. There is a possibility to present written or oral work in English / other language.
1 6.0 0.0 h/sem 36.0 h/sem 0.0 h/sem 0.0 h/sem 0.0 h/sem 0.0 h/sem 1.0 h/sem 37.0 h/sem 113.0 h/sem 0.0 h/sem 150.0 h/sem
Since year 2016/2017
Pre-requisites None
Objectives At the end of the learning unit students are expected to have developed the competencies that enable them to apply univariate and multivariate data analysis techniques to real situations in business and management.
Program 1. Data screening
1.1. The preparation and coding of the data; identification and correction of errors;
1.2. Using the statistical package SPSS.
2. Data analysis
2.1. Descriptive methods: exploring the data using descriptive statistics and plots;
2.2. Hypothesis testing
2.2.1. Parametric hypothesis tests: one-sample t-test; independent samples t-test; paired samples t-test; One-way ANOVA;
2.2.2.Non-parametric hypothesis tests: Kolmogorov-Smirnov test for normality; Chi-square test for independence;  Mann-Whitney and Kruskal-Wallis tests.
2.3. Multivariate methods
2.3.1. Principal Components Analysis;
2.3.2. Hierarchical Cluster Analysis and Non-Hierarchical K-means Clustering.
3. Applications with SPSS.

Evaluation Method For those in the continuous assessment system the overall course mark is a weighted average of three components: i) a group class presentation (10%); ii) group assignment on the statistical analysis of a data set (40%); iii) exam (50% of the final mark), with a minimum mark of 8 (out of 20). The final exam has two parts: a written component (50%) and a data analysis component using a computer (50%).
Teaching Method The following teaching-learning methodologies (LM) will be used:
LM1 ) Expositional, for the presentation of the theoretical reference frames
LM2) Participative, with analysis and resolution of application exercises
LM3) Active, with the realization of individual and group works
LM4) Experimental laboratory, with development and operation of computer models
LM5) Self-study, related with autonomous work by the student, as is contemplated in the Class Planning.

Observations
Basic Bibliographic Andy Field, Discovering Statistics using SPSS, SAGE, 3rd edition, 2009. (ISCTE-IUL Library code: MQ.124 FIE*Dis).

Joseph Hair et al., Multivariate data analysis: a global perspective, 7th ed, 2010 (the 6th edition is available at ISCTE-IUL - Library code: MQ124Mul).

Course notes (provided by the lecturer).



Complementar Bibliographic Brian Everitt and Graham Dunn, Applied Multivariate Data Analysis, Edward Arnold, 2nd edition, 2000. (ISCTE-IUL Library code: MQ.124 EVE*App).

Edwin Mansfield, Statistics for Business and Economics: Methods and Applications, 5th ed, 1994 (ISCTE-IUL Library code: MQ.121 MAN*Sta).