Global AI and Data Science

 View Only
Expand all | Collapse all

Survival Regression Models and Mixed Models Enhancements in SPSS Statistics Webinar

  • 1.  Survival Regression Models and Mixed Models Enhancements in SPSS Statistics Webinar

    Posted Wed August 31, 2022 02:39 PM
    Edited by System Fri January 20, 2023 04:35 PM
    Summary
    IBM® SPSS® Statistics enables organizations to gather rich insights from data with a powerful set of tools to validate assumptions, analyze past performance and forecast trends. The latest version introduces new statistical procedures, improved open source extension integration, UI enhancements, new data visualization features and other enhancements designed to improve everyday usability.
     
    In this webcast, you'll get an overview of what's new in SPSS Statistics 29. We will deep-dive into a new statistical procedure for fitting parametric accelerated failure time survival regression models, and also cover recent enhancements to linear and generalized linear mixed models procedures.
    Key takeaways: 
    • Get an overview of new statistical procedures and enhancements within SPSS Statistics 29 
    • How to use the new SURVREG procedure to fit parametric accelerated failure time models using Weibull, exponential, log-normal, or log-logistic distributions. 
    • Learn about enhancements to the MIXED procedure for linear mixed models and the GENLINMIXED procedure for generalized linear mixed models

    Please join us in this on-demand recording. Share your questions below and register to watch here.


    ------------------------------
    David Nichols
    IBM
    ------------------------------
    #GlobalAIandDataScience
    #GlobalDataScience


  • 2.  RE: Survival Regression Models and Mixed Models Enhancements in SPSS Statistics Webinar

    Posted Thu October 13, 2022 03:01 PM

    Hi everyone,

    You can watch the on-demand recording here and download the slides here.

    Please share any of your questions below.



    ------------------------------
    David Nichols
    IBM
    ------------------------------

    Original Message