This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, as well as how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results. 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. Terms and Conditions: Ingram Micro – https://www.ingrammicrotraining.com/Terms-of-use.aspx; IBM – http://www.ibm.com/training/terms

Version:

v25

Audience:

Anyone who has worked with IBM SPSS Statistics and wants to become better versed in the basic statistical capabilities of IBM SPSS Statistics Base. Anyone who wants to refresh their knowledge and statistical experience.

Objectives:

Detail:

ip between scale variables
? Chart the relationship between two scale variables
? Describe the relationship: Correlation
? Test on the correlation
? Assumptions for testing on the correlation
? Treatment of missing values

Predict a scale variable: Regression
? What is linear regression?
? Explain unstandardized and standardized coefficients
? Assess the fit of the model: R Square
? Examine residuals
? Include 0-1 independent variables
? Include categorical independent variables

Introduction to Bayesian statistics
? Bayesian statistics versus classical test theory
? Explain the Bayesian approach
? Evaluate a null hypothesis: Bayes Factor
? Bayesian procedures in IBM SPSS Statistics

Overview of multivariate procedures
? Overview of supervised models
? Overview of models to create natural groupings

Pre-Requisites:

Familiarity with basic concepts in statistics, such as measurement levels, mean, and standard deviation. Familiarity with the windows in IBM SPSS Statistics either by experience with IBM SPSS Statistics (version 18 or later) or completion of the IBM SPSS Statistics Essentials (V25) course.