(2/19/25)Study: insecticide exposure results in increased levels of octopamine, demonstrated by a normal distribution.
1 - 3
treatment <- rnorm(n=30, mean=0, sd=1)
print(treatment)## [1] -0.889272165 0.747338126 2.774336534 -0.019491518 1.056301789
## [6] 0.905883164 1.085164069 0.605559422 0.727508420 1.800878586
## [11] -1.951891767 0.132959667 0.409615039 0.138544513 1.477474738
## [16] 0.139436112 -0.008423824 -0.221916057 -0.679354323 -0.573096774
## [21] 0.572520691 -2.143824846 -0.890365914 -0.374589317 -0.895806063
## [26] 0.168093239 3.256838071 0.165999250 -2.105313802 -0.221360948control<- rnorm(n=30, mean=0, sd=1)
print(control)## [1] -0.08604622 0.54658382 -0.58949805 0.18923856 0.74032585 0.76623806
## [7] 2.29688712 -0.21616646 0.66596503 0.23432545 0.49872266 1.06375326
## [13] 0.22412586 -0.05174627 -2.49741028 0.24924042 -0.98977475 0.61327332
## [19] -0.47091449 0.96467109 0.48007276 0.86690296 -0.36098127 -1.35083333
## [25] -0.32846673 -0.94221428 -0.07426817 -0.33374820 -0.53681891 -1.58857384octopamine_data <- data.frame(treatment, control)
print(octopamine_data)## treatment control
## 1 -0.889272165 -0.08604622
## 2 0.747338126 0.54658382
## 3 2.774336534 -0.58949805
## 4 -0.019491518 0.18923856
## 5 1.056301789 0.74032585
## 6 0.905883164 0.76623806
## 7 1.085164069 2.29688712
## 8 0.605559422 -0.21616646
## 9 0.727508420 0.66596503
## 10 1.800878586 0.23432545
## 11 -1.951891767 0.49872266
## 12 0.132959667 1.06375326
## 13 0.409615039 0.22412586
## 14 0.138544513 -0.05174627
## 15 1.477474738 -2.49741028
## 16 0.139436112 0.24924042
## 17 -0.008423824 -0.98977475
## 18 -0.221916057 0.61327332
## 19 -0.679354323 -0.47091449
## 20 -0.573096774 0.96467109
## 21 0.572520691 0.48007276
## 22 -2.143824846 0.86690296
## 23 -0.890365914 -0.36098127
## 24 -0.374589317 -1.35083333
## 25 -0.895806063 -0.32846673
## 26 0.168093239 -0.94221428
## 27 3.256838071 -0.07426817
## 28 0.165999250 -0.33374820
## 29 -2.105313802 -0.53681891
## 30 -0.221360948 -1.58857384Justifications:
4 - 5
one.way <- aov(treatment ~ control, data = octopamine_data)
summary(one.way)## Df Sum Sq Mean Sq F value Pr(>F)
## control 1 0.01 0.0148 0.009 0.923
## Residuals 28 44.25 1.58026
treatmenti <- rnorm(n=20, mean=0, sd=1)
print(treatment)## [1] -0.889272165 0.747338126 2.774336534 -0.019491518 1.056301789
## [6] 0.905883164 1.085164069 0.605559422 0.727508420 1.800878586
## [11] -1.951891767 0.132959667 0.409615039 0.138544513 1.477474738
## [16] 0.139436112 -0.008423824 -0.221916057 -0.679354323 -0.573096774
## [21] 0.572520691 -2.143824846 -0.890365914 -0.374589317 -0.895806063
## [26] 0.168093239 3.256838071 0.165999250 -2.105313802 -0.221360948controli<- rnorm(n=20, mean=0, sd=1)
print(control)## [1] -0.08604622 0.54658382 -0.58949805 0.18923856 0.74032585 0.76623806
## [7] 2.29688712 -0.21616646 0.66596503 0.23432545 0.49872266 1.06375326
## [13] 0.22412586 -0.05174627 -2.49741028 0.24924042 -0.98977475 0.61327332
## [19] -0.47091449 0.96467109 0.48007276 0.86690296 -0.36098127 -1.35083333
## [25] -0.32846673 -0.94221428 -0.07426817 -0.33374820 -0.53681891 -1.58857384octopamine_datai <- data.frame(treatmenti, controli)
print(octopamine_datai)## treatmenti controli
## 1 0.06413693 -1.04169531
## 2 -0.19188010 -0.03915276
## 3 -0.23501387 0.15866264
## 4 -0.31076766 0.39868395
## 5 0.15338243 0.42880260
## 6 0.01333601 0.17932510
## 7 0.17344967 -0.94333101
## 8 0.32255267 0.41612320
## 9 0.80395856 -0.79808790
## 10 1.47604553 0.54141406
## 11 -0.20088452 -0.90473423
## 12 0.92993693 -0.19105583
## 13 -0.28444219 0.69381702
## 14 -1.59971250 -0.79359728
## 15 1.14851353 0.30670277
## 16 -2.56372166 -0.10581371
## 17 -0.28916089 -0.94997104
## 18 1.79113784 -0.20228472
## 19 -0.70553742 -0.10581081
## 20 0.06584573 0.67190219one.wayi <- aov(treatment ~ control, data = octopamine_datai)
summary(one.wayi)## Df Sum Sq Mean Sq F value Pr(>F)
## control 1 0.01 0.0148 0.009 0.923
## Residuals 28 44.25 1.5802for (i in octopamine_datai) {
cat (runif(1), "\n")
}## 0.385743
## 0.664898for (i in octopamine_datai) {
cat (runif(5), "\n")
}## 0.4585528 0.122128 0.7268232 0.8948519 0.7016464
## 0.6881399 0.9195752 0.4236908 0.1716034 0.1909444for (i in octopamine_datai) {
cat (runif(10), "\n")
}## 0.3836865 0.05602881 0.7300044 0.6097354 0.7958618 0.5960551 0.6266717 0.7373065 0.5586658 0.6931127
## 0.04368361 0.6048656 0.9292054 0.5847827 0.7751772 0.3762075 0.2152169 0.3536233 0.8046639 0.6498188for (i in octopamine_datai) {
cat (runif(30), "\n")
}## 0.07823656 0.6795328 0.6849925 0.6059702 0.2003833 0.3256155 0.4642588 0.686861 0.3748281 0.7094864 0.3339286 0.9570928 0.5790678 0.7969321 0.2602157 0.7171481 0.4425808 0.4664086 0.2900574 0.9034849 0.2983829 0.9113329 0.5930238 0.4664495 0.7999447 0.427955 0.3227209 0.4792971 0.4929393 0.5302236
## 0.9233353 0.5632224 0.4855202 0.3401094 0.5896199 0.5441773 0.2439284 0.02036514 0.2893337 0.1752595 0.09536132 0.2442016 0.4692029 0.2234139 0.7557077 0.6879054 0.8200641 0.8430348 0.724938 0.5018002 0.5701817 0.5708029 0.6593821 0.1413312 0.9521979 0.4053062 0.2222798 0.2527742 0.08488585 0.12186385 samples is the smallest effective size needed to detect a significant pattern between the treatment and control.
7
for (i in octopamine_data) {
cat (runif(1), "\n")
}## 0.04657274
## 0.9869222for (i in octopamine_data) {
cat (runif(5), "\n")
}## 0.043383 0.08339708 0.9098747 0.2187915 0.761474
## 0.6167883 0.9846216 0.2297398 0.05757827 0.1120004for (i in octopamine_data) {
cat (runif(10), "\n")
}## 0.7939202 0.966367 0.2610971 0.2347416 0.6759964 0.7510337 0.5212181 0.8060543 0.8220402 0.5388099
## 0.6937703 0.7117709 0.287617 0.1915395 0.2474704 0.5993306 0.8457274 0.5198012 0.3146289 0.9468839for (i in octopamine_data) {
cat (runif(30), "\n")
}## 0.04488975 0.8294424 0.5255793 0.3204998 0.65291 0.3338968 0.7495099 0.06359321 0.110585 0.9043576 0.9420397 0.5882778 0.3098895 0.8964419 0.8864933 0.133557 0.5331979 0.2632635 0.5074682 0.3743492 0.5841694 0.2299278 0.2636421 0.04173035 0.6920798 0.768203 0.7007681 0.04588464 0.6902411 0.2756624
## 0.4846845 0.6437756 0.7480376 0.008723675 0.9826233 0.1764154 0.6340868 0.4096638 0.09663832 0.4816725 0.2732433 0.482448 0.4631976 0.9712588 0.7782902 0.9858969 0.865487 0.985951 0.2405581 0.2199368 0.01351301 0.6949623 0.1893814 0.8974923 0.6034423 0.1728865 0.8351226 0.2489352 0.15367 0.69677425 samples is the smallest effective size needed to detect a significant pattern between the treatment and control that were originally hypothesized.