Regression Statistics Multiple R 0.475588 R Square 0.226184 Adjusted R Square 0.202002 Standard Error 0.074983 Observations 34 ANOVA dr SS F 9.353511 Regression Residual Total 1 32 33 MS 0.052590 0.005622 0.052590 0.179919 0.232509 Coefficients Standard Error Intercept 0.030940 0.013454 S&P 500 1.299582 0.424929 R(LUV) = Intercept + B, *R(S&P 500) + t Stat 2.299711 3.058351 P-value 0.028138 0.004473 Regression Statistics Multiple R 0.481580 R Square 0.231920 Adjusted R Square 0.182366 Standard Error 0.075900 Observations 34 ANOVA al F 4.6801817 Regression Residual Total 2 31 33 SS 0.0539234 0.1785856 0.2325090 MS 0.0269617 0.0057608 Intercept S&P 500 US Gas Price Coefficients -0.003963 1.254744 0.032202 Standard Error 0.073811 0.440105 0.066930 I Stat -0.053686 2.851012 0.481130 P-value 0.957530 0.007685 0.633803 R(LUV) = Intercept + B, * R(S&P 500) + B2 * US Gas Prices + 12. Based on the two regression results for the South West Airlines (LUV), should you include US Gas Prices as one of the variables for your model and why? A. Yes, Gas Prices can be used as a proxy for one the major costs for the South West Airlines and therefore should be included in the model B. Yes, R Squared is higher for the model that includes US Gas Pieces, hence US Gas Prices add explanatory power to the 2nd model C. No, Adjusted R Squared has decreased in the 2nd model, therefore US Gas Prices do not add meaningful explanatory power to the model D. No, coefficient for the US Gas Prices is substantially less than 1, hence, it will have insignificant effect on the model's predictive power. E. Cannot be determined based on the information provided