iris <- read.csv("E:/Summer 2026/data/iris.csv")
deer = read.csv("E:/Summer 2026/data/Deer.csv")
aragorn = rnorm(50, mean=180, sd=10)
gimli = rnorm(50, mean=132, sd=15)
legolas = rnorm(50, 195, 15)Module09 (5/29/2026)
This module explores simple inference tests in R. In this module, we will run:
1. t.tests
2. Analysis of Variance
3. Correlation Tests
4. Chi-squared Tests
Project
Variables
Test 1
t.test(legolas, gimli, alternative="two.sided")
Welch Two Sample t-test
data: legolas and gimli
t = 19.989, df = 97.69, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
52.08915 63.57240
sample estimates:
mean of x mean of y
192.3782 134.5474 t.test(legolas, aragorn, alternative="two.sided")
Welch Two Sample t-test
data: legolas and aragorn
t = 5.8117, df = 80.406, p-value = 1.195e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
9.380038 19.147960
sample estimates:
mean of x mean of y
192.3782 178.1142 There is a significant difference in height between Legolas and Gimli actors and between the height of Legolas and Aragorn actors.
Test 2
var.test(legolas,gimli)
F test to compare two variances
data: legolas and gimli
F = 1.1193, num df = 49, denom df = 49, p-value = 0.6947
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.635193 1.972470
sample estimates:
ratio of variances
1.11933 There is no significant difference between the variances of the height of Legolas actors compared to Gimli actors.
Test 3
#Setosa
cor.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
0.7425467 #Versicolor
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
0.5259107 #Virginica
cor.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 Setosa: Strong positive correlation
Veriscolor: Moderate positive correlation
Virginica: Moderate positive correlation
Test 4
table(deer$Month)
1 2 3 4 5 6 7 8 9 10 11 12
256 165 27 3 2 35 11 19 58 168 189 188 chisq.test(table(deer$Month))
Chi-squared test for given probabilities
data: table(deer$Month)
X-squared = 997.07, df = 11, p-value < 2.2e-16table(deer$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-15Deer observations are not evenly distributed across months of the year. There is a significant association between farms and TB in deer.
DISCLAIMER:
ChatGPT was used during the process of writing the code for the purpose of debugging and fixing errors in the code.