Advanced Statistical Analysis Using IBM SPSS Statistics (V26) – 0K09BG

Course Name:

Advanced Statistical Analysis Using IBM SPSS Statistics (V26)

0K09BG

Skill Level:

Advanced

Modality:

SPVC – Self-Paced Virtual Training

Duration:

2 Day/s

Price:
Request Quote

Overview:

In this course you will learn how to use advanced statistical procedures, such as Factor, Cluster, Decision Trees, Cox Regression, and Linear Mixed models, using IBM SPSS Statistics (V26).

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.


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Target Audience:

IBM SPS Statistics users who want to learn advanced statistical methods to be able to better answer research questions.

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Prerequisites:

  • Experience with IBM SPSS Statistics (version 18 or later) 
  • Knowledge of statistics, either by on the job experience, intermediate-level statistics oriented courses, or completion of the Statistical Analysis Using IBM SPSS Statistics (V26) course. 

Topic:

Introduction to advanced statistical analysis 

  • Taxonomy of models
  • Overview of supervised models
  • Overview of models to CR – Classroom Trainingeate 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 DisCR – Classroom Trainingiminant Analysis

  • DisCR – Classroom Trainingiminant Analysis basics
  • The DisCR – Classroom Trainingiminant Analysis model
  • Assumptions of DisCR – Classroom Trainingiminant 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

Objective:

Introduction to advanced statistical analysis 

  • Taxonomy of models
  • Overview of supervised models
  • Overview of models to CR – Classroom Trainingeate 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 DisCR – Classroom Trainingiminant Analysis

  • DisCR – Classroom Trainingiminant Analysis basics
  • The DisCR – Classroom Trainingiminant Analysis model
  • Assumptions of DisCR – Classroom Trainingiminant 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

Remarks:

Category:

Data and AI

Product Name:

IBM SPSS Statistics

Badge and Certification Info:

0

Brand: 

Analytics

Follow on Courses:

 

 

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Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

spss;statistics;ibm spss;analytics;analysis

VMWare-Based (simple) (10)

Lab Access Duration: 30

 

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