#read file
deer <- read.csv("deer.csv")#make a vector of legolas actors, compare to aragorns and gimlis
aragorn = rnorm(50, mean=180, sd=10)
gimli = rnorm(50, mean=132, sd=15)
legolas = rnorm(50, mean=195, sd=15)
t.test(aragorn, legolas, alternative="two.sided")##
## Welch Two Sample t-test
##
## data: aragorn and legolas
## t = -6.7443, df = 84.694, p-value = 1.78e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -20.44653 -11.13545
## sample estimates:
## mean of x mean of y
## 178.2497 194.0407t.test(legolas, gimli, alternative="two.sided")##
## Welch Two Sample t-test
##
## data: legolas and gimli
## t = 23.887, df = 96.483, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 57.12229 67.47553
## sample estimates:
## mean of x mean of y
## 194.0407 131.7418#run variance test to compare gimli and legolas actors
var.test(legolas, gimli)##
## F test to compare two variances
##
## data: legolas and gimli
## F = 1.2868, num df = 49, denom df = 49, p-value = 0.3806
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7302115 2.2675320
## sample estimates:
## ratio of variances
## 1.28677#redo correlation for sepal length and sepal width for iris dataset for individual species
cor.test(iris$Sepal.Length [iris$Species == "versicolor"], iris$Sepal.Width [iris$Species == "versicolor"])##
## Pearson's product-moment correlation
##
## data: iris$Sepal.Length[iris$Species == "versicolor"] and iris$Sepal.Width[iris$Species == "versicolor"]
## t = 4.2839, df = 48, p-value = 8.772e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2900175 0.7015599
## sample estimates:
## cor
## 0.5259107cor.test(iris$Sepal.Length [iris$Species == "setosa"], iris$Sepal.Width [iris$Species == "setosa"])##
## Pearson's product-moment correlation
##
## data: iris$Sepal.Length[iris$Species == "setosa"] and iris$Sepal.Width[iris$Species == "setosa"]
## t = 7.6807, df = 48, p-value = 6.71e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5851391 0.8460314
## sample estimates:
## cor
## 0.7425467cor.test(iris$Sepal.Length [iris$Species == "virginica"], iris$Sepal.Width [iris$Species == "virginica"])##
## Pearson's product-moment correlation
##
## data: iris$Sepal.Length[iris$Species == "virginica"] and iris$Sepal.Width[iris$Species == "virginica"]
## t = 3.5619, df = 48, p-value = 0.0008435
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2049657 0.6525292
## sample estimates:
## cor
## 0.4572278#use deer dataset and chisq.test() to see if there are siginificant differences in number of deer caught per month
chisq.test(table(deer$Month))##
## Chi-squared test for given probabilities
##
## data: table(deer$Month)
## X-squared = 997.07, df = 11, p-value < 2.2e-16#use the chisq.test() to see fi the cases of tuberculosis are uniformly distributed across all farms
table(deer$Farm, deer$Tb)##
## 0 1
## AL 10 3
## AU 23 0
## BA 67 5
## BE 7 0
## CB 88 3
## CRC 4 0
## HB 22 1
## LCV 0 1
## LN 28 6
## MAN 27 24
## MB 16 5
## MO 186 31
## NC 24 4
## NV 18 1
## PA 11 0
## PN 39 0
## QM 67 7
## RF 23 1
## RN 21 0
## RO 31 0
## SAL 0 1
## SAU 3 0
## SE 16 10
## TI 9 0
## TN 16 2
## VISO 13 1
## VY 15 4chisq.test(table(deer$Farm, deer$Tb))## Warning in chisq.test(table(deer$Farm, deer$Tb)): Chi-squared approximation may
## be incorrect##
## Pearson's Chi-squared test
##
## data: table(deer$Farm, deer$Tb)
## X-squared = 129.09, df = 26, p-value = 1.243e-15