SC <- c(0.105091778, 0.052280224, 0.036203149, 0.086488105, 0.129323975, 0.088875751) OC <- c(0.119430252, 0.095096894, 0.182395322, 0.152515324, 0.136396623, 0.22228637) #homogeneity of variance var.test(SC, OC) shapiro.test(SC) shapiro.test(OC) t.test(x = OC,y = SC, var.equal = TRUE) #SCvsBP SC2 <- c(0.118735, 0.1058893, 0.1109217, 0.1704554, 0.1461373, 0.06633724, 0.1205224, 0.1056609, 0.1691358, 0.1214047, 0.1382275, 0.1318536, 0.0839929, 0.07968158, 0.1396061) BP <- c(0.214700, 0.217811, 0.215514, 0.207567, 0.213861, 0.116598, 0.161690, 0.176370, 0.208725, 0.189213, 0.209630, 0.186218, 0.214861, 0.116677177, 0.217575) var.test(SC2, BP) shapiro.test(SC2) shapiro.test(BP) #not normal t.test(x = SC2,y = BP, var.equal = TRUE) wilcox.test(x = SC2, y = BP) ################# ##ACI Means at Mid Frequency bins ACI_SC <- c(0.549305011, 0.482988681, 0.457600495, 0.502505432, 0.543821028, 0.507976498) ACI_OC <- c(0.49164918, 0.545078448, 0.541232066, 0.572838736, 0.572602005, 0.640028798) var.test(ACI_SC, ACI_OC) shapiro.test(ACI_SC) shapiro.test(ACI_OC) t.test(x = ACI_SC,y = ACI_OC, var.equal = TRUE) #####2019 Data MFC_SC2019_June <- c(0.165807442, 0.194520523, 0.236549049, 0.172279372, 0.145426105, 0.184951038, 0.180083902, 0.183083715,0.167498785, 0.164674777, 0.114700261, 0.150100477, 0.177501991, 0.201494491, 0.1803297, 0.186556288, 0.13197872) MFC_OC2019_June <- c(0.195912767, 0.197027026, 0.117472214, 0.091906559, 0.150644878, 0.176257829, 0.131994633, 0.151819879,0.178070362, 0.129837944, 0.061294196, 0.133141267, 0.163365073, 0.180509312, 0.201188246, 0.157425505,0.175368629) var.test(MFC_SC2019_June, MFC_OC2019_June) shapiro.test(MFC_SC2019_June) shapiro.test(MFC_OC2019_June) t.test(MFC_SC2019_June, MFC_OC2019_June)