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.

â¢ Taxonomy of models
â¢ Overview of supervised models
â¢ Overview of models to create natural groupings

Group variables: Factor Analysis and Principal Components Analysis
â¢ Factor Analysis basics
â¢ Principal Components basics
â¢ Assumptions of Factor Analysis
â¢ Key issues in Factor Analysis
â¢ Improve the interpretability
â¢ Use Factor and component scores

Group similar cases: Cluster Analysis
â¢ Cluster Analysis basics
â¢ Key issues in Cluster Analysis
â¢ K-Means Cluster Analysis
â¢ Assumptions of K-Means Cluster Analysis
â¢ TwoStep Cluster Analysis
â¢ Assumptions of TwoStep Cluster Analysis

Predict categorical targets with Nearest Neighbor Analysis
â¢ Nearest Neighbor Analysis basics
â¢ Key issues in Nearest Neighbor Analysis
â¢ Assess model fit

Predict categorical targets with Discriminant Analysis
â¢ Discriminant Analysis basics
â¢ The Discriminant Analysis model
â¢ Core concepts of Discriminant Analysis
â¢ Classification of cases
â¢ Assumptions of Discriminant Analysis
â¢ Validate the solution

Predict categorical targets with Logistic Regression
â¢ Binary Logistic Regression basics
â¢ The Binary Logistic Regression model
â¢ Multinomial Logistic Regression basics
â¢ Assumptions of Logistic Regression procedures
â¢ Testing hypotheses

Predict categorical targets with Decision Trees
â¢ Decision Trees basics
â¢ Validate the solution
â¢ Explore CHAID
â¢ Explore CRT
â¢ Comparing Decision Trees methods

Introduction to Survival Analysis
â¢ Survival Analysis basics
â¢ Kaplan-Meier Analysis
â¢ Assumptions of Kaplan-Meier Analysis
â¢ Cox Regression
â¢ Assumptions of Cox Regression

Introduction to Generalized Linear Models
â¢ Generalized Linear Models basics
â¢ Available distributions