(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] 0.82694611 0.29024420 -1.28520055 0.89358465 2.28776905 0.76859788
## [7] -1.64933752 -0.36807139 0.25304252 0.25552065 -1.03519004 -1.29818614
## [13] 1.44021467 4.06424607 -0.06645707 0.33553729 0.52811172 -1.36183270
## [19] 0.26855288 0.29009497 0.87789772 -1.83117871 1.23870690 1.02093943
## [25] -0.40666697 0.79005834 -0.02350382 -1.14897612 -0.42291022 -0.10870582control<- rnorm(n=30, mean=0, sd=1)
print(control)## [1] -0.16085142 0.83508001 -0.04840319 0.39093414 1.25455787 2.31194988
## [7] -0.48754656 0.32614674 0.70635611 0.12432073 1.13126620 1.89716674
## [13] 0.93254013 -0.30578155 -0.86479024 1.59983426 -2.32654076 0.81965777
## [19] 0.46734175 0.96230843 2.00548841 0.51801806 -2.74477473 -1.35083109
## [25] -0.87776092 1.37366642 -2.09202918 2.78232596 0.26402121 1.17426427octopamine_data <- data.frame(treatment, control)
print(octopamine_data)## treatment control
## 1 0.82694611 -0.16085142
## 2 0.29024420 0.83508001
## 3 -1.28520055 -0.04840319
## 4 0.89358465 0.39093414
## 5 2.28776905 1.25455787
## 6 0.76859788 2.31194988
## 7 -1.64933752 -0.48754656
## 8 -0.36807139 0.32614674
## 9 0.25304252 0.70635611
## 10 0.25552065 0.12432073
## 11 -1.03519004 1.13126620
## 12 -1.29818614 1.89716674
## 13 1.44021467 0.93254013
## 14 4.06424607 -0.30578155
## 15 -0.06645707 -0.86479024
## 16 0.33553729 1.59983426
## 17 0.52811172 -2.32654076
## 18 -1.36183270 0.81965777
## 19 0.26855288 0.46734175
## 20 0.29009497 0.96230843
## 21 0.87789772 2.00548841
## 22 -1.83117871 0.51801806
## 23 1.23870690 -2.74477473
## 24 1.02093943 -1.35083109
## 25 -0.40666697 -0.87776092
## 26 0.79005834 1.37366642
## 27 -0.02350382 -2.09202918
## 28 -1.14897612 2.78232596
## 29 -0.42291022 0.26402121
## 30 -0.10870582 1.17426427Justifications:
4 - 5
one.way <- aov(treatment ~ control, data = octopamine_data)
summary(one.way)## Df Sum Sq Mean Sq F value Pr(>F)
## control 1 0.86 0.8642 0.564 0.459
## Residuals 28 42.94 1.53366
treatmenti <- rnorm(n=20, mean=0, sd=1)
print(treatment)## [1] 0.82694611 0.29024420 -1.28520055 0.89358465 2.28776905 0.76859788
## [7] -1.64933752 -0.36807139 0.25304252 0.25552065 -1.03519004 -1.29818614
## [13] 1.44021467 4.06424607 -0.06645707 0.33553729 0.52811172 -1.36183270
## [19] 0.26855288 0.29009497 0.87789772 -1.83117871 1.23870690 1.02093943
## [25] -0.40666697 0.79005834 -0.02350382 -1.14897612 -0.42291022 -0.10870582controli<- rnorm(n=20, mean=0, sd=1)
print(control)## [1] -0.16085142 0.83508001 -0.04840319 0.39093414 1.25455787 2.31194988
## [7] -0.48754656 0.32614674 0.70635611 0.12432073 1.13126620 1.89716674
## [13] 0.93254013 -0.30578155 -0.86479024 1.59983426 -2.32654076 0.81965777
## [19] 0.46734175 0.96230843 2.00548841 0.51801806 -2.74477473 -1.35083109
## [25] -0.87776092 1.37366642 -2.09202918 2.78232596 0.26402121 1.17426427octopamine_datai <- data.frame(treatmenti, controli)
print(octopamine_datai)## treatmenti controli
## 1 -0.3553199 0.55378276
## 2 -0.6979598 0.79958907
## 3 -0.1570099 0.41135989
## 4 0.3862406 -1.18634589
## 5 1.2497419 0.20788685
## 6 2.7774548 0.24044553
## 7 -0.1335324 -0.15695652
## 8 -0.1265866 -0.99648287
## 9 0.5318200 0.79764909
## 10 -0.2805106 -0.20196407
## 11 -2.1692734 -1.77223905
## 12 -1.4987381 0.34033891
## 13 0.5990210 -0.81639866
## 14 -0.5824945 0.27599810
## 15 1.8582612 2.29213615
## 16 -0.6229670 -0.39553650
## 17 0.6325481 -0.83416089
## 18 -0.2559590 0.01157377
## 19 0.2117909 0.57772314
## 20 -1.6234489 0.24141590one.wayi <- aov(treatment ~ control, data = octopamine_datai)
summary(one.wayi)## Df Sum Sq Mean Sq F value Pr(>F)
## control 1 0.86 0.8642 0.564 0.459
## Residuals 28 42.94 1.5336for (i in octopamine_datai) {
cat (runif(1), "\n")
}## 0.9373784
## 0.9512989for (i in octopamine_datai) {
cat (runif(5), "\n")
}## 0.9611106 0.261555 0.5751754 0.7129989 0.4760041
## 0.4393675 0.9862704 0.9303315 0.8663868 0.4845957for (i in octopamine_datai) {
cat (runif(10), "\n")
}## 0.5300793 0.7118675 0.2223397 0.807264 0.2440162 0.2257437 0.7663192 0.5213036 0.8882378 0.4002041
## 0.917427 0.9579989 0.1513106 0.2147268 0.5289201 0.5560151 0.2216921 0.878847 0.6555255 0.6408489for (i in octopamine_datai) {
cat (runif(30), "\n")
}## 0.5411727 0.9295477 0.8138859 0.08076364 0.7729096 0.4899382 0.2668818 0.6759762 0.3747922 0.2788437 0.9331301 0.9112809 0.02735342 0.640661 0.8583466 0.9571392 0.9151514 0.03799598 0.9623642 0.2191293 0.1805656 0.9152962 0.1134703 0.4999089 0.7204925 0.09359789 0.1925766 0.8442433 0.8916142 0.1980913
## 0.7756923 0.4659459 0.9896336 0.4723466 0.7653791 0.8611126 0.7574491 0.4821624 0.1556754 0.8228044 0.646536 0.3830588 0.8309857 0.2529226 0.6488423 0.7700153 0.5135653 0.7480902 0.5442042 0.8293535 0.818632 0.6012985 0.4185855 0.08941423 0.7638999 0.8176626 0.6819049 0.7214324 0.6235624 0.48404135 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")
}## 0.5960358
## 0.6885891for (i in octopamine_data) {
cat (runif(5), "\n")
}## 0.2603954 0.2312702 0.2763749 0.4465433 0.72547
## 0.5381686 0.2025213 0.8418246 0.980571 0.9797124for (i in octopamine_data) {
cat (runif(10), "\n")
}## 0.5918736 0.9126074 0.424658 0.1295003 0.8551706 0.3043202 0.1058358 0.04739403 0.6691295 0.04672241
## 0.6114994 0.5890479 0.3253021 0.06376477 0.4307906 0.7187658 0.386991 0.2501062 0.8375803 0.1950649for (i in octopamine_data) {
cat (runif(30), "\n")
}## 0.7869121 0.8490908 0.2055114 0.87289 0.2232447 0.4269507 0.9217664 0.5255363 0.584198 0.8978161 0.8869438 0.8549219 0.1978433 0.5954002 0.3165594 0.5028762 0.4442608 0.5304711 0.554037 0.002627158 0.1706671 0.664652 0.2168329 0.4379874 0.65083 0.80118 0.4323703 0.2804418 0.4086741 0.5054831
## 0.4738759 0.4236857 0.2760796 0.1599881 0.05131227 0.6484899 0.7339784 0.8896297 0.1360166 0.7987055 0.8247615 0.4698952 0.8596736 0.6506589 0.2981381 0.2540191 0.6602871 0.1396318 0.1872441 0.2277439 0.9430021 0.07836229 0.7615479 0.6586073 0.8208377 0.7908166 0.9293393 0.4903125 0.9722656 0.92694875 samples is the smallest effective size needed to detect a significant pattern between the treatment and control that were originally hypothesized.