Load in the required packages
library(rstatix)##
## Attaching package: 'rstatix'## The following object is masked from 'package:stats':
##
## filterlibrary(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.5 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.0.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks rstatix::filter(), stats::filter()
## x dplyr::lag() masks stats::lag()Load in the file titled “LabAssignment5”
lab5<-read.csv("/cloud/project/LabAssignment5.csv", stringsAsFactors = TRUE)
lab5## Q1 Habitat Cholesterol.Content Q2 Microbe Humidity.Level
## 1 NA Desert 98 NA 1 Low
## 2 NA Desert 85 NA 2 Low
## 3 NA Desert 81 NA 3 Low
## 4 NA Desert 99 NA 4 Low
## 5 NA Desert 94 NA 5 Low
## 6 NA Desert 98 NA 6 Low
## 7 NA Desert 94 NA 7 Low
## 8 NA Desert 85 NA 8 Low
## 9 NA Antarctic 26 NA 1 High
## 10 NA Antarctic 20 NA 2 High
## 11 NA Antarctic 21 NA 3 High
## 12 NA Antarctic 20 NA 4 High
## 13 NA Antarctic 28 NA 5 High
## 14 NA Antarctic 26 NA 6 High
## 15 NA Antarctic 23 NA 7 High
## 16 NA Antarctic 29 NA 8 High
## Cholesterol.Content.1
## 1 98
## 2 85
## 3 81
## 4 99
## 5 94
## 6 98
## 7 94
## 8 85
## 9 81
## 10 76
## 11 90
## 12 92
## 13 91
## 14 86
## 15 79
## 16 93You are studying biochemical adaptations in lipid membranes of microbes in different biomes. You count the number of cholesterol molecules per 〖μm〗^2 in the membrane for microbes in 5 different biomes. Does the data below suggest that environment influences the cholesterol content in the microbe membranes?
Ho: p1=p2
Ha: p1>p2
var.test(Cholesterol.Content ~ Habitat, data = lab5) ##
## F test to compare two variances
##
## data: Cholesterol.Content by Habitat
## F = 0.26151, num df = 7, denom df = 7, p-value = 0.0977
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.05235546 1.30622266
## sample estimates:
## ratio of variances
## 0.2615108t.test(Cholesterol.Content~Habitat, data=lab5, paired=TRUE)##
## Paired t-test
##
## data: Cholesterol.Content by Habitat
## t = -25.896, df = 7, p-value = 3.274e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -73.79988 -61.45012
## sample estimates:
## mean of the differences
## -67.625Lab5_stats<-lab5 %>%
group_by(Habitat) %>%
get_summary_stats(Cholesterol.Content)
Lab5_stats## # A tibble: 2 × 14
## Habitat variable n min max median q1 q3 iqr mad mean sd
## <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Antarct… Cholest… 8 20 29 24.5 20.8 26.5 5.75 5.19 24.1 3.60
## 2 Desert Cholest… 8 81 99 94 85 98 13 6.67 91.8 7.05
## # … with 2 more variables: se <dbl>, ci <dbl>Conclusion:
You know that temperature can impact the cholesterol content in cell membranes, but what about humidity? You take the desert microbes and place them in a habitat with higher humidity for 1 week, and record the cholesterol content after. Is there a difference in the cholesterol content between the two treatments?
head(lab5)## Q1 Habitat Cholesterol.Content Q2 Microbe Humidity.Level
## 1 NA Desert 98 NA 1 Low
## 2 NA Desert 85 NA 2 Low
## 3 NA Desert 81 NA 3 Low
## 4 NA Desert 99 NA 4 Low
## 5 NA Desert 94 NA 5 Low
## 6 NA Desert 98 NA 6 Low
## Cholesterol.Content.1
## 1 98
## 2 85
## 3 81
## 4 99
## 5 94
## 6 98Ho: p1=p2
Ha: p1=/=p2
var.test(Cholesterol.Content.1 ~ Humidity.Level, data = lab5) ##
## F test to compare two variances
##
## data: Cholesterol.Content.1 by Humidity.Level
## F = 0.86331, num df = 7, denom df = 7, p-value = 0.8512
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1728378 4.3121518
## sample estimates:
## ratio of variances
## 0.8633094t.test(Cholesterol.Content.1 ~ Humidity.Level, data=lab5, paired=TRUE)##
## Paired t-test
##
## data: Cholesterol.Content.1 by Humidity.Level
## t = -1.6531, df = 7, p-value = 0.1423
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -13.974751 2.474751
## sample estimates:
## mean of the differences
## -5.75Lab51_stats<-lab5 %>%
group_by(Humidity.Level) %>%
get_summary_stats(Cholesterol.Content.1)
Lab51_stats## # A tibble: 2 × 14
## Humidity.Level variable n min max median q1 q3 iqr mad mean
## <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 High Cholest… 8 76 93 88 80.5 91.2 10.8 6.67 86
## 2 Low Cholest… 8 81 99 94 85 98 13 6.67 91.8
## # … with 3 more variables: sd <dbl>, se <dbl>, ci <dbl>Conclusion: Fail to reject null hypothesis