Advanced Statistical Analysis Using IBM SPSS Statistics (V26) - SPVC [0K09BG]
Beschreibung
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OVERVIEW
Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.
This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.
OBJECTIVES
• Introduction to advanced statistical analysis
• Grouping variables w…
Frequently asked questions
Es wurden noch keine FAQ hinterlegt. Falls Sie Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice. Wir helfen gerne weiter!
Ontdek de verschillende trainingsmogelijkheden bij Global Knowledge
Online of op locatie er is altijd een vorm die bij je past.
Kies op welke manier jij of je team graag een training wilt volgen. Global Knowledge bied je verschillende trainingsmogelijkheden. Je kunt kiezen uit o.a. klassikaal, Virtueel Klassikaal (online), e-Learning en maatwerk. Met onze Blended oplossing kun je de verschillende trainingsvormen combineren.
OVERVIEW
Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.
This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.
OBJECTIVES
• Introduction to advanced statistical analysis
• Grouping variables with Factor Analysis and Principal Components
Analysis
• Grouping cases with Cluster Analysis
• Predicting categorical targets with Nearest Neighbor
Analysis
• Predicting categorical targets with Discriminant
Analysis
• Predicting categorical targets with Logistic Regression
• Predicting categorical targets with Decision Trees
• Introduction to Survival Analysis
• Introduction to Generalized Linear Models
• Introduction to Linear Mixed Models
CONTENT
Introduction to advanced statistical analysis
• Taxonomy of models
• Overview of supervised models
• Overview of models to create natural groupings
Grouping variables with Factor Analysis and Principal Components
Analysis
• Factor Analysis basics
• Principal Components basics
• Assumptions of Factor Analysis
• Key issues in Factor Analysis
• Use Factor and component scores
Grouping cases with Cluster Analysis
• Cluster Analysis basics
• Key issues in Cluster Analysis
• K-Means Cluster Analysis
• Assumptions of K-Means Cluster Analysis
• TwoStep Cluster Analysis
• Assumptions of TwoStep Cluster Analysis
Predicting categorical targets with Nearest Neighbor
Analysis
• Nearest Neighbors Analysis basics
• Key issues in Nearest Neighbor Analysis
• Assess model fit
Predicting categorical targets with Discriminant Analysis
• Discriminant Analysis basics
• The Discriminant Analysis model
• Assumptions of Discriminant Analysis
• Validate the solution
Predicting categorical targets with Logistic Regression
• Binary Logistic Regression basics
• The Binary Logistic Regression model
• Multinomial Logistic Regression basics
• Assumptions of Logistic Regression procedures
• Test hypotheses
• ROC curves
Predicting categorical targets with Decision Trees
• Decision Trees basics
• Explore CHAID
• Explore C&RT
• Compare Decision Trees methods
Introduction to Survival Analysis
• Survival Analysis basics
• Kaplan-Meier Analysis
• Assumptions of Kaplan-Meier Analysis
• Cox Regression
• Assumptions of Cox Regression
Introduction to Generalized Linear Models
• Generalized Linear Models basics
• Available distributions
• Available link functions
Introduction to Linear Mixed Models
• Linear Mixed Models basics
• Hierarchical Linear Models
• Modeling strategy
• Assumptions of Linear Mixed Models
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