Finding the most 'important' variable

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  • Thread starter Thread starter gheelengooi
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SUMMARY

This discussion centers on analyzing landslide data to identify the most significant variables affecting landslides, such as slope angle and rainfall intensity. The user is considering employing multiple regression analysis and sensitivity analysis for this purpose, utilizing tools like MATLAB and Excel. A suggestion is made to explore Principal Component Analysis (PCA) as a potential method for variable importance assessment.

PREREQUISITES
  • Understanding of multiple regression analysis
  • Familiarity with sensitivity analysis techniques
  • Proficiency in MATLAB for data analysis
  • Basic knowledge of Principal Component Analysis (PCA)
NEXT STEPS
  • Research the implementation of multiple regression analysis in MATLAB
  • Explore sensitivity analysis methods applicable to landslide data
  • Learn how to conduct Principal Component Analysis (PCA) using MATLAB
  • Investigate data visualization techniques in Excel for landslide data presentation
USEFUL FOR

Data analysts, environmental scientists, and researchers focused on landslide risk assessment and modeling who seek to understand the impact of various environmental factors on landslides.

gheelengooi
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I am trying to analyze a junk of landslide data (landslide @ different locations) to see which factor has the most effect on landslide, that includes slope angle, rainfall intensity etc etc, which sums up to 7 to 8 of them.

I am wondering which kind of analysis I should adapt in order to do that. I am thinking of using multiple regression analysis and sensitivity analysis but I am still trying to figure it out.

Any good suggestion for that?

(I use Matlab and Excel)
 
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Well, nobody here can do your research or give much help without knowing anything about it, but you might start by reading about http://en.wikipedia.org/wiki/Principal_component_analysis" .
 
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