SleepStudy <- read.csv("https://www.lock5stat.com/datasets3e/SleepStudy.csv")
Question 1:
- Is there a significant difference in the average GPA between male
and female college students?
t.test(GPA ~ Gender, data = SleepStudy)
##
## Welch Two Sample t-test
##
## data: GPA by Gender
## t = 3.9139, df = 200.9, p-value = 0.0001243
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## 0.09982254 0.30252780
## sample estimates:
## mean in group 0 mean in group 1
## 3.324901 3.123725
Question 2:
- Is there a significant difference in the average number of early
classes between the first two class years and other class years?
first_two <- SleepStudy$EarlyClass[SleepStudy$ClassYear <= 2]
upper_years <- SleepStudy$EarlyClass[SleepStudy$ClassYear > 2]
t.test(first_two, upper_years, na.rm = TRUE)
##
## Welch Two Sample t-test
##
## data: first_two and upper_years
## t = 2.3233, df = 224.26, p-value = 0.02106
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02121868 0.25831438
## sample estimates:
## mean of x mean of y
## 0.7253521 0.5855856
Question 3:
- Do students who identify as “larks” have significantly better
cognitive skills (cognition z-score) than “owls”?
larks <- SleepStudy$CognitionZscore[SleepStudy$LarkOwl == "Lark"]
owls <- SleepStudy$CognitionZscore[SleepStudy$LarkOwl == "Owl"]
t.test(larks, owls, na.rm = TRUE)
##
## Welch Two Sample t-test
##
## data: larks and owls
## t = 0.80571, df = 75.331, p-value = 0.4229
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1893561 0.4465786
## sample estimates:
## mean of x mean of y
## 0.09024390 -0.03836735
Question 4:
- Is there a significant difference in the average number of classes
missed in a semester between students who had at least one early class
(EarlyClass=1) and those who didn’t (EarlyClass=0)?
early <- SleepStudy$ClassesMissed[SleepStudy$EarlyClass == 1]
no_early <- SleepStudy$ClassesMissed[SleepStudy$EarlyClass == 0]
t.test(early, no_early, na.rm = TRUE)
##
## Welch Two Sample t-test
##
## data: early and no_early
## t = -1.4755, df = 152.78, p-value = 0.1421
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.5412830 0.2233558
## sample estimates:
## mean of x mean of y
## 1.988095 2.647059
Question 5:
- Is there a significant difference in the average happiness level
between students with at least moderate depression and normal depression
status?
mod_sev <- SleepStudy$Happiness[SleepStudy$DepressionStatus %in% c("moderate", "severe")]
normal <- SleepStudy$Happiness[SleepStudy$DepressionStatus == "normal"]
t.test(mod_sev, normal, na.rm = TRUE)
##
## Welch Two Sample t-test
##
## data: mod_sev and normal
## t = -5.6339, df = 55.594, p-value = 6.057e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.379724 -3.507836
## sample estimates:
## mean of x mean of y
## 21.61364 27.05742
Question 6:
- Is there a significant difference in average sleep quality scores
between students who reported having at least one all-nighter
(AllNighter=1) and those who didn’t (AllNighter=0)?
alln <- SleepStudy$PoorSleepQuality[SleepStudy$AllNighter == 1]
none <- SleepStudy$PoorSleepQuality[SleepStudy$AllNighter == 0]
t.test(alln, none, na.rm = TRUE)
##
## Welch Two Sample t-test
##
## data: alln and none
## t = 1.7068, df = 44.708, p-value = 0.09479
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1608449 1.9456958
## sample estimates:
## mean of x mean of y
## 7.029412 6.136986
Question 7:
- Do students who abstain from alcohol use have significantly better
stress scores than those who report heavy alcohol use?
abstain <- SleepStudy$StressScore[SleepStudy$AlcoholUse == "Abstain"]
heavy <- SleepStudy$StressScore[SleepStudy$AlcoholUse == "Heavy"]
t.test(abstain, heavy, na.rm = TRUE)
##
## Welch Two Sample t-test
##
## data: abstain and heavy
## t = -0.62604, df = 28.733, p-value = 0.5362
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.261170 3.327346
## sample estimates:
## mean of x mean of y
## 8.970588 10.437500
Question 8:
- Is there a significant difference in the average number of drinks
per week between students of different genders?
male_drinks <- SleepStudy$Drinks[SleepStudy$Gender == 1]
female_drinks <- SleepStudy$Drinks[SleepStudy$Gender == 0]
t.test(male_drinks, female_drinks, na.rm = TRUE)
##
## Welch Two Sample t-test
##
## data: male_drinks and female_drinks
## t = 6.1601, df = 142.75, p-value = 7.002e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.241601 4.360009
## sample estimates:
## mean of x mean of y
## 7.539216 4.238411
Question 9:
- Is there a significant difference in the average weekday bedtime
between students with high and low stress (Stress=High
vs. Stress=Normal)?
high_stress <- SleepStudy$WeekdayBed[SleepStudy$Stress == "high"]
normal_stress <- SleepStudy$WeekdayBed[SleepStudy$Stress == "normal"]
t.test(high_stress, normal_stress, na.rm = TRUE)
##
## Welch Two Sample t-test
##
## data: high_stress and normal_stress
## t = -1.0746, df = 87.048, p-value = 0.2855
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4856597 0.1447968
## sample estimates:
## mean of x mean of y
## 24.71500 24.88543
Question 10:
- Is there a significant difference in the average hours of sleep on
weekends between first two-year students and other students?
lower_sleep <- SleepStudy$WeekendSleep[SleepStudy$ClassYear <= 2]
upper_sleep <- SleepStudy$WeekendSleep[SleepStudy$ClassYear > 2]
t.test(lower_sleep, upper_sleep, na.rm = TRUE)
##
## Welch Two Sample t-test
##
## data: lower_sleep and upper_sleep
## t = -0.047888, df = 237.36, p-value = 0.9618
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3497614 0.3331607
## sample estimates:
## mean of x mean of y
## 8.213592 8.221892