Hello! I am conducting multivariate linear regression using -regress- of Google trend interest (a 100-point scale) as a function of racial/ethnic breakdown of a state. I'm wondering if this is actually an appropriate approach to analyzing this data. The IVs are the proportion of White, Black, Asian, mixed race, Native American/Pacific Islander, etc residents in a state. The DV is -interest100-.

Obviously, the proportion of each of these variables affects the others, raising concerns for multicollinearity. In the multivariate model, I excluded white race to try to mitigate this; I also used robust standard errors to try to account for heteroscedasticity and some outliers that I could not justify removing. However, is it appropriate to do multivariate regression at all? Some of the IVs with very small values have extremely high coefficients in the multivariate model; should I not include them?


Thank you so much! Data below:

Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float interest100 double(white black asian americanindian hispaniclatino mixedrace nativehawiianpacificislander)
.86 .691 .268  .015 .007 .046 .018 .001
.74 .653 .037  .065 .156 .073 .075 .014
.93 .826 .052  .037 .053 .317 .029 .003
.79  .79 .157  .017  .01 .078 .022 .004
.91 .719 .065  .155 .016 .394  .04 .005
.72 .869 .046  .035 .016 .218 .031 .002
.85 .797 .122   .05 .006 .169 .025 .001
 .9 .692 .232  .041 .007 .096 .027 .001
.82  .46  .46  .045 .006 .113 .029 .001
.94 .773 .169   .03 .005 .264 .022 .001
.95 .602 .326  .044 .005 .099 .022 .001
.73 .255 .022  .376 .004 .107 .242 .101
.66 .867 .022  .049 .018 .134  .04 .005
.84 .768 .146  .059 .006 .175 .021 .001
.81 .848 .099  .026 .004 .073 .022 .001
 .7 .906 .041  .027 .005 .063  .02 .002
.69 .863 .061  .032 .012 .122 .031 .001
.77 .875 .085  .016 .003 .039  .02 .001
 .8 .628 .328  .018 .008 .053 .018 .001
.63 .944 .017  .013 .007 .018 .018    .
  1 .585 .311  .067 .006 .106 .029 .001
.87 .806  .09  .072 .005 .124 .026 .001
.86 .792 .141  .034 .007 .053 .025    .
 .7 .838  .07  .052 .014 .056 .026 .001
 .9 .591 .378  .011 .006 .034 .013 .001
.75 .829 .118  .022 .006 .044 .024 .002
.72 .889 .006  .009 .067 .041 .028 .001
.69 .881 .052  .027 .015 .114 .023 .001
.89 .739 .103  .087 .017 .292 .046 .008
 .7 .931 .018   .03 .003  .04 .018    .
.96 .719 .151    .1 .006 .209 .023 .001
.87 .819 .026  .018  .11 .493 .026 .002
.96 .696 .176   .09  .01 .193 .027 .001
.82 .706 .222  .032 .016 .098 .023 .001
 .8 .869 .034  .017 .056 .041 .023 .001
.72 .817 .131 .0255 .003  .04 .024 .001
.72  .74 .078  .024 .094 .111 .063 .002
.68 .867 .022  .049 .018 .134  .04 .005
.84 .816  .12  .038 .004 .078 .021 .001
.78 .836 .085  .037 .011 .163 .029 .002
.88 .686  .27  .018 .005  .06  .02 .001
 .7 .846 .023  .015  .09 .042 .025 .001
.79 .784 .171   .02 .005 .057  .02 .001
.94 .787 .129  .052  .01 .397 .021 .001
.75 .906 .015  .027 .016 .144 .026 .011
.69 .942 .014  .019 .004  .02  .02    .
.91 .694 .199  .069 .005 .098 .032 .001
.77 .785 .044  .096 .019  .13 .049 .008
.71 .935 .036  .008 .003 .017 .018    .
.66  .87 .067   .03 .012 .071  .02 .001
.76 .925 .013  .011 .027 .101 .022 .001
end