Advanced Statistical Analysis Using IBM SPSS Statistics V25 SPVC 0K09AG

Duration: 
2 days
Codes: 
0K09AG
Versions: 
V25

Overview

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.

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

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

Audience

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

Skills Gained

  • 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
  • Introduction to Survival Analysis
  • Introduction to Generalized Linear Models
  • Introduction to Linear Mixed Models

Prerequisites

  • 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.

Course Outline

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
  • Validate the solution
  • Explore CHAID
  • Explore CRT
  • 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

Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.