37. We ran a regression on the per capita death rate from Covid-19 using state level data. The explanatory variables were vaxination rate, obesity rate, and population density. All the variables are of the expected sign and statistically significant. Do you think we have a multicolinearity probem? Explain. . regress deathpc vax obesity density Source Model Residual Total SS vax obesity density _cons 99006.6009 101407.179 200413.78 deathpc Coefficient df B 33002 2003 46 2204.50389 MS 49 4090.07714 Std. err. -3.004803 1.101435 5.166446 2.2322 .1807242 029574 133.2389 110.251 Number of obs = F(3, 46) Prob > F R-squared Adj R-squared = Root MSE E t P>|t| -2.73 0.009 2.31 0.025 6.11 0.000 1.21 0.233 = -5.221877 .6732606 1211947 -88.68488 50 14.97 0.0000 0.4940 0.4610 46.952 [95% conf. interval] 7877287 9.65963 .2402536 355.1628
37. We ran a regression on the per capita death rate from Covid-19 using state level data. The explanatory variables were vaxination rate, obesity rate, and population density. All the variables are of the expected sign and statistically significant. Do you think we have a multicolinearity probem? Explain. . regress deathpc vax obesity density Source Model Residual Total SS vax obesity density _cons 99006.6009 101407.179 200413.78 deathpc Coefficient df B 33002 2003 46 2204.50389 MS 49 4090.07714 Std. err. -3.004803 1.101435 5.166446 2.2322 .1807242 029574 133.2389 110.251 Number of obs = F(3, 46) Prob > F R-squared Adj R-squared = Root MSE E t P>|t| -2.73 0.009 2.31 0.025 6.11 0.000 1.21 0.233 = -5.221877 .6732606 1211947 -88.68488 50 14.97 0.0000 0.4940 0.4610 46.952 [95% conf. interval] 7877287 9.65963 .2402536 355.1628
Chapter1: Making Economics Decisions
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