library(faux)
##
## ************
## Welcome to faux. For support and examples visit:
## https://debruine.github.io/faux/
## - Get and set global package options with: faux_options()
## ************
set.seed(1002528620)
data = round(rnorm_multi(n = 120,vars = 3,r = 0.6,varnames = c("L","a","b"),mu = c(39.0, 4.2, 38.9),sd = c(1.20,0.25,1.12)),2)
data
## L a b
## 1 39.77 4.85 40.71
## 2 38.71 4.36 38.96
## 3 38.02 4.28 38.63
## 4 37.45 3.99 38.06
## 5 38.42 4.44 39.66
## 6 38.05 4.41 38.02
## 7 40.69 4.51 39.54
## 8 38.67 4.52 37.87
## 9 38.92 3.83 36.95
## 10 40.14 4.24 39.03
## 11 41.41 4.76 42.03
## 12 37.97 4.03 37.63
## 13 38.43 4.14 38.45
## 14 39.26 3.58 38.17
## 15 39.40 4.42 40.63
## 16 38.90 4.23 39.28
## 17 38.95 4.18 40.49
## 18 39.12 3.95 39.44
## 19 37.50 4.07 38.70
## 20 41.57 4.73 41.18
## 21 38.13 4.35 38.13
## 22 40.18 4.04 38.73
## 23 38.22 3.97 36.72
## 24 39.22 4.42 39.83
## 25 38.82 4.27 38.43
## 26 39.78 4.67 38.83
## 27 39.12 4.21 38.83
## 28 38.69 4.17 37.65
## 29 40.74 4.44 39.68
## 30 38.40 4.43 38.86
## 31 37.66 3.90 38.99
## 32 36.81 3.58 36.46
## 33 37.54 4.01 38.46
## 34 35.27 3.69 37.28
## 35 37.68 4.38 39.70
## 36 37.42 3.89 38.51
## 37 37.63 3.87 38.09
## 38 38.23 3.66 37.43
## 39 41.37 4.27 39.47
## 40 37.47 4.18 38.13
## 41 38.72 4.47 39.61
## 42 40.08 4.40 39.85
## 43 37.14 4.01 36.83
## 44 40.18 4.65 39.48
## 45 39.67 4.71 41.61
## 46 38.00 4.13 36.83
## 47 39.94 4.55 39.70
## 48 38.99 4.14 39.63
## 49 38.49 4.07 38.76
## 50 38.20 4.28 38.12
## 51 40.34 4.47 39.00
## 52 37.73 4.05 39.14
## 53 38.92 3.91 37.29
## 54 40.74 4.64 39.29
## 55 37.47 3.91 38.95
## 56 37.88 3.94 37.48
## 57 38.03 4.09 39.38
## 58 37.60 4.19 38.11
## 59 40.33 4.64 40.46
## 60 37.84 4.38 39.36
## 61 37.62 4.17 38.23
## 62 38.47 4.48 38.94
## 63 37.51 3.65 36.98
## 64 36.94 4.22 38.91
## 65 39.19 4.37 38.47
## 66 40.04 4.76 40.79
## 67 39.12 4.16 39.02
## 68 38.35 4.10 38.24
## 69 38.52 4.30 38.18
## 70 40.19 4.44 40.03
## 71 38.89 3.85 38.61
## 72 39.56 4.29 38.73
## 73 39.57 3.87 38.89
## 74 41.02 4.31 40.90
## 75 41.36 4.50 39.55
## 76 39.29 4.12 38.92
## 77 39.17 4.10 38.30
## 78 40.00 4.08 39.70
## 79 37.11 3.81 37.39
## 80 39.04 4.10 40.41
## 81 39.53 4.23 38.50
## 82 38.74 4.18 38.72
## 83 38.06 3.95 39.05
## 84 40.11 4.71 39.83
## 85 40.17 4.30 38.71
## 86 38.21 3.97 37.28
## 87 38.53 4.05 37.32
## 88 39.92 4.54 41.45
## 89 38.63 4.20 36.61
## 90 36.70 4.09 37.67
## 91 36.81 4.08 39.07
## 92 40.28 4.17 39.71
## 93 38.20 3.75 37.74
## 94 40.39 4.25 40.07
## 95 38.43 4.46 39.09
## 96 39.07 4.34 38.17
## 97 38.56 4.14 38.47
## 98 39.36 3.95 39.60
## 99 38.84 4.21 38.18
## 100 38.95 3.91 38.29
## 101 39.20 4.08 36.19
## 102 38.42 4.03 38.46
## 103 39.29 4.16 39.44
## 104 39.67 4.37 39.58
## 105 40.60 4.25 39.83
## 106 38.76 3.99 38.99
## 107 38.88 4.48 37.80
## 108 40.55 4.28 38.65
## 109 39.85 4.33 39.56
## 110 40.16 4.44 40.04
## 111 38.85 4.18 38.40
## 112 39.31 4.07 37.76
## 113 40.23 4.20 38.78
## 114 39.43 4.12 38.32
## 115 38.46 3.78 37.99
## 116 42.00 4.56 40.70
## 117 40.42 4.47 40.14
## 118 39.63 4.24 39.22
## 119 39.46 4.51 39.69
## 120 37.59 3.92 39.36
IC=(data[2]*1000)/(data[1]*data[3])
colnames(IC)[1]="IC"
IC
## IC
## 1 2.995608
## 2 2.890975
## 3 2.914117
## 4 2.799318
## 5 2.913888
## 6 3.048399
## 7 2.803188
## 8 3.086519
## 9 2.663247
## 10 2.706387
## 11 2.734906
## 12 2.820527
## 13 2.801777
## 14 2.388969
## 15 2.761081
## 16 2.768339
## 17 2.650459
## 18 2.560126
## 19 2.804479
## 20 2.763088
## 21 2.991959
## 22 2.596115
## 23 2.828767
## 24 2.829465
## 25 2.862213
## 26 3.023324
## 27 2.771506
## 28 2.862677
## 29 2.746568
## 30 2.968723
## 31 2.656018
## 32 2.667476
## 33 2.777415
## 34 2.806370
## 35 2.928011
## 36 2.699431
## 37 2.700012
## 38 2.557743
## 39 2.615021
## 40 2.925673
## 41 2.914522
## 42 2.754842
## 43 2.931573
## 44 2.931338
## 45 2.853389
## 46 2.950970
## 47 2.869544
## 48 2.679310
## 49 2.728115
## 50 2.939189
## 51 2.841234
## 52 2.742505
## 53 2.694087
## 54 2.898778
## 55 2.679080
## 56 2.775151
## 57 2.730997
## 58 2.924066
## 59 2.843570
## 60 2.940816
## 61 2.899432
## 62 2.990611
## 63 2.631352
## 64 2.935988
## 65 2.898571
## 66 2.914467
## 67 2.725255
## 68 2.795765
## 69 2.923791
## 70 2.759811
## 71 2.564029
## 72 2.799971
## 73 2.514820
## 74 2.568966
## 75 2.750968
## 76 2.694278
## 77 2.732949
## 78 2.569270
## 79 2.745861
## 80 2.598874
## 81 2.779411
## 82 2.786643
## 83 2.657708
## 84 2.948207
## 85 2.765308
## 86 2.787004
## 87 2.816530
## 88 2.743726
## 89 2.969784
## 90 2.958432
## 91 2.836945
## 92 2.607034
## 93 2.601154
## 94 2.626006
## 95 2.968922
## 96 2.910209
## 97 2.790880
## 98 2.534235
## 99 2.839010
## 100 2.621706
## 101 2.875978
## 102 2.727334
## 103 2.684568
## 104 2.783194
## 105 2.628165
## 106 2.640194
## 107 3.048316
## 108 2.730885
## 109 2.746650
## 110 2.761183
## 111 2.801909
## 112 2.741949
## 113 2.692102
## 114 2.726748
## 115 2.587100
## 116 2.667603
## 117 2.755078
## 118 2.727936
## 119 2.879641
## 120 2.649468
mean(IC$IC)
## [1] 2.781107
t.test(IC,alternative = "l",mu = 2.8)
##
## One Sample t-test
##
## data: IC
## t = -1.5643, df = 119, p-value = 0.0602
## alternative hypothesis: true mean is less than 2.8
## 95 percent confidence interval:
## -Inf 2.801129
## sample estimates:
## mean of x
## 2.781107