IBM Training for Advanced Statistical Analysis Using IBM

Advanced Statistical Analysis Using IBM SPSS Statistics (V26)

IBM Training for Advanced Statistical Analysis Using IBM SPSS Statistics (V26)

Skill Level: Advanced

Modality: CR – Classroom based Training or ILO – Instructor Led Online Class

Duration: 2 Day/s

Starting Price: $ – 1,306

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).

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..

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 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 AnalysisNearest Neighbors Analysis basics

Key issues in Nearest Neighbor Analysis
Assess model fit

Predicting categorical targets with Discriminant AnalysisDiscriminant 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

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

IBM Training

Objective:

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 AnalysisNearest Neighbors Analysis basics

Key issues in Nearest Neighbor Analysis
Assess model fit

Predicting categorical targets with Discriminant AnalysisDiscriminant 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

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

Category: Data, Analytics, and AI

Product Name:

IBM SPSS Statistics

Badge and Certification Info:

Badge Title:

Badge ID: NONE

Brand: Analytics

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