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    Multivariate Data Analysis
	Name: Multivariate Data Analysis
      
      
	Code: MAT13613M
      
      
	6 ECTS
      
      
	Duration: 15 weeks/156 hours
      
      
	Scientific Area:
	
	      
	      
	      	      	  		  	      	  		  	   	      	  	   			   
		  		  Mathematics
	      	
      
      
	Teaching languages: Portuguese
      
            	        	  	   	        	  	   	        	  	   	        	  	   	              
      
	Languages of tutoring support: Portuguese
      
                  
	Regime de Frequência: Presencial
      
      
      
            
            Sustainable Development Goals
Learning Goals
		  		      The main objective of the curricular unit is to provide students with tools and silks necessary in multivariate quantitative research, including inferential methods for dealing with uncertainties in drawing conclusions from multivariate collected data. At the conclusion of the course it is expected that students will be able to specify conceptualized models and to deal with theoretical issues related with dependent, interdependent and extensions multivariate statistical techniques using adequate statistical packages (SPSS/EXCEL/AMOS/R...). Students will also be prepared to critique results reported in scientific literature. 
All the techniques will be accomplished with the resolution of exercises related with scientific areas of interest.
	  All the techniques will be accomplished with the resolution of exercises related with scientific areas of interest.
Contents
		  		      1.	Overview of Multivariate Statistical Methods. Introduction. Dependence Techniques and Interdependence Techniques. Extentions.
2. Preliminary and exploratory multivariate data analysis
3. Principal Component Analysis
4. Exploratory Factorial Analysis versus Confirmatory Factorial Analysis
5. Cluster Analysis
6. Structural Equation Modeling: an introduction
	  2. Preliminary and exploratory multivariate data analysis
3. Principal Component Analysis
4. Exploratory Factorial Analysis versus Confirmatory Factorial Analysis
5. Cluster Analysis
6. Structural Equation Modeling: an introduction
Teaching Methods
		  		      The methodology incorporates several innovative strategies for enhancing student motivation and performance. Learning techniques, such as the use of hands-on activities and cooperative learning assignments, are preferred, as they allow students to construct their own understanding of statistical concepts and applications by actively engaging the course material (projection of slides about statistical theory followed by the resolution of exercises). For each activity, it is also provided a summary of the critical procedural steps and a list of materials needed.
Evaluation
The student may choose between continuous assessment system and exam evaluation system.
Continuous assessment consists of the preparation and exposure to colleagues and teacher of periodic reports on the subject taught.The final grade corresponds to the arithmetic average of the required works.
The evaluation by exam consists of the examinations on the taught subject.
	  Evaluation
The student may choose between continuous assessment system and exam evaluation system.
Continuous assessment consists of the preparation and exposure to colleagues and teacher of periodic reports on the subject taught.The final grade corresponds to the arithmetic average of the required works.
The evaluation by exam consists of the examinations on the taught subject.
Teaching Staff
- Luís Miguel Lindinho da Cunha Mendes Grilo [responsible]
 
            
    
    
      
            
    
    
      
            
    
    
      
            
    
    
      
            
    
    
      
            
    
    
      
            
    
    
      
            
    
    
      
            
    
    
      
            
    
    
      
            
    
    
      