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Advanced Statistical Analysis Using IBM SPSS Statistics (V26) - SPVC

  • Tuition USD $575 GSA  $492.44
  • Reviews star_rate star_rate star_rate star_rate star_half 2368 Ratings
  • Course Code 0K09BG-SPVC
  • Duration 2 days
  • Available Formats Self Paced
This course is available in other formats
Self-Paced
IBM SPSS Statistics Essentials (V26) - SPVC (0K53BG-SPVC)

This IBM Self-Paced Virtual Class (SPVC) includes:
- PDF course guide available to attendee during and after course
- Lab environment where students can work through demonstrations and exercises at their own pace

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.

Skills Gained

- Introduction to advanced statistical analysis 
- Grouping variables with Factor Analysis and Principal Components Analysis 
- Grouping cases with Cluster Analysis 
- Predicting categorical targets with Nearest Neighbor Analysis 
- Predicting categorical targets with Discriminant Analysis 
- Predicting categorical targets with Logistic Regression 
- Predicting categorical targets with Decision Trees 
- Introduction to Survival Analysis 
- Introduction to Generalized Linear Models 
- Introduction to Linear Mixed Models

Who Can Benefit

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. 

Course Details

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 

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

When does class start/end?

Classes begin promptly at 9:00 am, and typically end at 5:00 pm.

Does the course schedule include a Lunchbreak?

Lunch is normally an hour long and begins at noon. Coffee, tea, hot chocolate and juice are available all day in the kitchen. Fruit, muffins and bagels are served each morning. There are numerous restaurants near each of our centers, and some popular ones are indicated on the Area Map in the Student Welcome Handbooks - these can be picked up in the lobby or requested from one of our ExitCertified staff.

How can someone reach me during class?

If someone should need to contact you while you are in class, please have them call the center telephone number and leave a message with the receptionist.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does ExitCertified deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

Does ExitCertified deliver group training?

Yes, we provide training for groups, individuals, and private on sites. View our group training page for more information.

It should include certification practice questions in relation to the topic discussed.

Training is Good and we learnt more on this course. Trainer also good and he clarified all questions asked by us.

the class was setup very well and I feel that I will be able to take what I have learned and use.

The course and labs were well organized and performed flawlessly. They made sure that all the information was timely relayed to the students.

The software installation instructions needs to be more clear. We spent a lot time on installing and setting up the environment.

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