Survival Regression Models and Mixed Models Enhancements in SPSS Statistics

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When:  Oct 13, 2022

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



Yingda Jiang is an Analytics Statistician, IBM Software

Yingda Jiang is Analytics Statistician, IBM SPSS Statistics. He holds a PhD in Biostatistics from the University of Pittsburgh, with a concentration on statistical genetics. Yingda joined IBM at the end of 2015. He currently works for the SPSS Statistics team on the design of algorithm, output, and user interfaces.

David Nichols is a Lead Statistician, SPSS Statistics, IBM Software

David holds a PhD in Research Methodology and Quantitative Psychology from the University of Chicago. He leads statistical planning and design for IBM SPSS Statistics product. He previously was Lead Statistician for Watson Machine Learning Visualization
Yingda Jiang is Analytics Statistician, IBM SPSS Statistics. He holds a PhD in Biostatistics from the University of Pittsburgh, with a concentration on statistical genetics. Yingda joined IBM at the end of 2015. He currently works for the SPSS Statistics team on the design of algorithm, output, and user interfaces.




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