SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Statistics Regression

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Summary Output Multiple R R Square Adjusted R Square Standard Error Observations Anova Regression Statistics Regression 1 (69.07 KiB) Viewed 15 times

SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Statistics Regression Residual Total Intercept GDP per cap Total Cases per million KOFGI own vaccine Election 0.69695424 0.48574521 0.43327023 21.2234507 df 55 SS MS Significance F 5 20847.7052 4169.54104 9.25670149 2.9696E-06 49 22071.3081 450.43486 54 42919.0133 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 3.03114018 18.7759644 0.16143726 0.87241284 -34.700573 40.7628532 34.700573 40.7628532 0.00051168 0.00014364 3.56230109 0.00083022 0.00022303 0.00080033 0.00022303 0.00080033 0.00016705 0.00016545 1.00971165 0.31759367 -0.0001654 0.00049953 0.0001654 0.00049953 0.07770027 0.32251644 0.24091878 0.81062331 -0.5704208 0.72582132 -0.5704208 0.72582132 25.185731 11.5030636 2.18948028 0.03335404 2.06945914 48.3020028 2.06945914 48.3020028 -1.8530929 7.64375082 -0.2424324 0.80945701 17.213785 13.5075995 -17.213785 13.5075995 1. Discuss the strength and the significance of your regression model by using R-square and significancef where a = 0.05 2. Now check for multicollinearity using Excel. Copy and paste your results to your word document. This can be a table including the VIF for each and every variable. Explain if you see a multicollinearity issue in your regression. What are the consequences of multicollinearity for a regression, in general? If your regression has a multicollinearity problem, how would you proceed to fix the issue?