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I'm inexperienced and confused in statistics, so I need help. I have a data table, values are temperature, particulate matter(PM), and vegetation indexes. And idea is that when PM increases, vegetation index should drop down and temperature should also increase.

What method should I use, when I want to build a model to predict PM?

I looked at multiple regression which is for a relation between two or more explanatory variables and a response variable. I have one explanatory (PM).

              Temp   PM    TVI    VI
          1   30.44  17   1.02   0.16
          2   26.95  13   1.06   0.24
          11   ..    

I'm still working on how approach this, so thanks for any help

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  • $\begingroup$ What approaches have you tried so far? I'm confused by what you mean by " When i looked at multiple regresion which is relation between two or more explanatory variables and a response variable. I have one explonatory (PM)." Isn't PM your response variable? $\endgroup$ – Eric Jul 14 '15 at 15:01
  • $\begingroup$ Your question is very unclear. You say you want to "predict PM", but later say that PM is your one explanatory variable? Which is it? Is PM your explanatory variable or your response variable? $\endgroup$ – AdmiralWen Jul 14 '15 at 15:15
  • $\begingroup$ I think mixing effect model is simple to try. This paper use AOD and environmental condition(temp & RH & Wind, etc) to predict PM2.5 concentration. Please click here $\endgroup$ – Han Zhengzu Feb 21 '16 at 16:48

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