Residuals : Min 10 Median 30 Max -0.75474 -0. 07262 0.00005 0. 07000 0.81046 Coefficients: Estimate Std. Error t value Pr(>Itl) (Intercept) 1.537188 0. 450917 3.409 0.090990 *** log(pricePerCase) -0.223671 0. 086702 -2.580 0.011565 * Log(numSoldLstYr) 0.917233 0. 023132 39.652 confint(elastObj03) 2.5 % 97.5 % (Intercept) 0. 64094179 2.4334349207 Log(pricePerCase) -0. 39600091 -0.0513417393 log(numSoldLstYr) 0. 87125474 0.9632105572 log(priceRatio) -0. 02671992 0.1124726154 log(printAdvert) 0. 01426823 0.0498380625 Log(outdoorAdvert) -0.03778376 0.0005575035 Log(broadcastAdvert) -0.00740632 0.0310418289A. From the first model above (the one using the full svedkaData dataframe): - What would you tell SVEDKA with respect to the elasticities of price, previous sales and the various forms of advertising? What appears to have the higher elasticities? Is there anything that is inelastic