Question #1:
-H0: There is no difference between male and female body temperatures.
-HA: There is a difference between male and female body temperatures.
getwd()
## [1] "/Users/serenamadsen/Documents/geog6000/lab02"
normtemp <- read.csv("../datafiles/normtemp.csv")
normtemp$fsex <- factor(normtemp$sex,
levels = c(1, 2),
labels = c("male", "female"))
maletemp <- normtemp[1:65, 1:5]
femaletemp <- normtemp[66:130, 1:5]
t.test(maletemp$temp, femaletemp$temp, alternative = 'two.sided')
##
## Welch Two Sample t-test
##
## data: maletemp$temp and femaletemp$temp
## t = -2.2854, df = 127.51, p-value = 0.02394
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.53964856 -0.03881298
## sample estimates:
## mean of x mean of y
## 98.10462 98.39385
-The p-value is below 0.05 so I have sufficient evidence to reject the null and accept the alternative hypothesis. (There is evidence for a difference in body temperature between men and women.)
Question #2:
-H0:There is not difference of life expectancy based on region.
-HA:There is a difference of life expectancy based on region.
gapc <- read.csv("../datafiles/gapc.csv")
boxplot(gapc$lifeexpectancy ~ gapc$continent, ylab = 'Life Expectancy (years)', xlab = 'Continent')
aov(lifeexpectancy ~ continent, data = gapc)
## Call:
## aov(formula = lifeexpectancy ~ continent, data = gapc)
##
## Terms:
## continent Residuals
## Sum of Squares 9757.236 7141.470
## Deg. of Freedom 6 165
##
## Residual standard error: 6.578878
## Estimated effects may be unbalanced
## 1 observation deleted due to missingness
summary(aov(lifeexpectancy ~ continent, data = gapc))
## Df Sum Sq Mean Sq F value Pr(>F)
## continent 6 9757 1626.2 37.57 <2e-16 ***
## Residuals 165 7141 43.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
-Life expectancy does vary significantly across continenents, as our p-value is far less than 0.05.