- CMDBID
**1001604** - Course Code
**0G09BG** - Duration
**2 Days**

Scroll

course overview

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.

Audience

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

Skills Gained

Introduction to advanced statistical analysis

- Taxonomy of models
- Overview of supervised models
- Overview of models to create 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 Discriminant Analysis

- Discriminant Analysis basics
- The Discriminant Analysis model
- Assumptions of Discriminant 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

- Explore CHAID
- Explore C&RT

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

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.

Outline

Introduction to advanced statistical analysis

- Taxonomy of models
- Overview of supervised models
- Overview of models to create 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 Discriminant Analysis

- Discriminant Analysis basics
- The Discriminant Analysis model
- Assumptions of Discriminant 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

- Explore CHAID
- Explore C&RT

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

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. Its a cost effective option. One on one training can be delivered too, at reasonable rates.

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.

**All $ prices are in USD unless it’s a NZ or AU date**

**SPVC = Self Paced Virtual Class**

**LVC = Live Virtual Class**

Our clients have included prestigious national organisations such as Oxford University Press, multi-national private corporations such as JP Morgan and HSBC, as well as public sector institutions such as the Department of Defence and the Department of Health.