 # Analyse statistique avancée avec IBM SPSS Statistics (V25)

Par Global Knowledge

### Objectifs

In this course, you will discover:

• Introduction to advanced statistical analysis
• Group variables: Factor Analysis and Principal Components Analysis
• Group similar cases: Cluster Analysis
• Predict categorical targets with Nearest Neighbor Analysis
• Predict categorical targets with Discriminant Analysis
• Predict categorical targets with Logistic Regression
• Predict categorical targets with Decision Trees
• Introduction to Survival Analysis
• Introduction to Generalized Linear Models
• Introduction to Linear Mixed Models

### Programme

• Taxonomy of models
• Overview of supervised models
• Overview of models to create natural groupings

Group variables: Factor Analysis and Principal Components Analysis

• Factor Analysis basics
• Principal Components basics
• Assumptions of Factor Analysis
• Key issues in Factor Analysis
• Improve the interpretability
• Use Factor and component scores

Group similar cases: 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

Predict categorical targets with Nearest Neighbor Analysis

• Nearest Neighbor Analysis basics
• Key issues in Nearest Neighbor Analysis
• Assess model fit

Predict categorical targets with Discriminant Analysis

• Discriminant Analysis basics
• The Discriminant Analysis model
• Core concepts of Discriminant Analysis
• Classification of cases
• Assumptions of Discriminant Analysis
• Validate the solution

Predict categorical targets with Logistic Regression

• Binary Logistic Regression basics
• The Binary Logistic Regression model
• Multinomial Logistic Regression basics
• Assumptions of Logistic Regression procedures
• Testing hypotheses

Predict categorical targets with Decision Trees

• Decision Trees basics
• Validate the solution
• Explore CHAID
• Explore CRT
• Comparing 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

Introduction to Linear Mixed Models

• Linear Mixed Models basics
• Hierachical Linear Models
• Modeling strategy
• Assumptions of Linear Mixed Models

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