Advanced Statistical Analysis Using IBM SPSS Statistics (V25)

Durée: 

2

Langue: 

FR

Prix: 
1716.8
Description: 
  • 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

Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions.

  • Experience with IBM SPSS Statistics (navigation through windows; using dialog boxes)
  • Knowledge of statistics, either by on the job experience, intermediate-level statistics oriented courses, or completion of the Statistical Analysis Using IBM SPSS Statistics (V25) course.

Introduction to advanced statistical analysis
• 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
• Available link functions

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

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