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Advanced Statistical Analysis Using IBM SPSS Statistics (V26)
Course Description
Overview
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.
Objectives
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
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
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.
Topics
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
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
Related Courses
-
IBM SPSS Statistics Essentials (V26)
0G53BG- Duration: 16 Hours
- Delivery Format: Classroom Training, Online Training
- Price: 1,630.00 USD
-
IBM SPSS Statistics Essentials (V26)
0K53BGS- Duration: 16 Hours
- Delivery Format: Self-Paced Training
- Price: 925.00 USD
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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Self-Paced Training Terms & Conditions
THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 365 DAYS TO ACCESS THE COURSE.
This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor. If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.
Upon receipt of the Order Confirmation Letter which includes your Enrollment Key (Access code); the course begins its twelve (12) month access period. IMPORTANT!!! If your course provides access to a hands-on lab (Virtual Lab Environment), you will have a specific number of days (typically 30 days) on the remote lab platform to complete your hands-on labs. Do not start your lab until you are ready to use your lab time effectively. Time allotted in the virtual lab environment will be indicated once you apply the enrollment key. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Note: This does not add additional days to your Lab Environment time frame.
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