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soo i have a ratio variable(salary) and three other variables(country by hdi index, soo interval variable, and college degree type, soo nominal variable and personality(extraversion, introversion), soo another nominal variable). soo i would like too calculate salary based on all other variables. How can i do that? I can obviously calculate Pearson coefficient for country and salary, but how about for the others? What would i need to calculate, to get estimated salary based on the three variables? I hope my question is clear enough. Thanks in advance.

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  • $\begingroup$ So you are trying to predict salary based on your 3 independent variables? $\endgroup$ – user2974951 Feb 11 '19 at 9:58
  • $\begingroup$ Exactly, is it possible? $\endgroup$ – Magna Snipe Feb 11 '19 at 15:41
  • $\begingroup$ i'm not really sure if they are independent though? Do you mean independent between each other or in relationship with salary? $\endgroup$ – Magna Snipe Feb 11 '19 at 15:44
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Simplest option is a regulard linear model, that is you are trying to predict your dependent variable salary, with your independent variables (independent between them) country, degree and personality. Note: you would really need some additional continous variables to get a better prediction.

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  • $\begingroup$ Maybe age? I'm not really familiar with linear model? I tried running it in SPSS and it got me a few coefficients. Soo from those coefficients I can calculate the dependent variable? $\endgroup$ – Magna Snipe Feb 13 '19 at 9:29
  • $\begingroup$ @MagnaSnipe Age could help, but other variables which are related with salary would also be good. Anyway, those coefficients give you an idea of how much each variable influences the outcome (salary) (very roughly). To get the predictions out you need to use a predict function (I don't use SPSS so I can't help you there, but there must be a function). $\endgroup$ – user2974951 Feb 13 '19 at 9:35
  • $\begingroup$ Ok, great, thank you for your help, I appreciate it! $\endgroup$ – Magna Snipe Feb 13 '19 at 10:10

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