##setting up heights of actors
aragorn = rnorm(50, mean=180, sd=10) ##data for aragorn actors heights
gimli = rnorm(50, mean=132, sd=15) ##data for gimli actors heights
legolas = rnorm(50, 195, 15) ##data for legolas actors heightsmodule09
##t-tests
t.test(legolas, aragorn, alternative="two.sided") ##p value is less then 0.05 at 95% confidence meaning there is substanial difference between heights of legolas and aragorn actors
Welch Two Sample t-test
data: legolas and aragorn
t = 6.0019, df = 97.902, p-value = 3.301e-08
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
8.561653 17.020028
sample estimates:
mean of x mean of y
194.3599 181.5691 t.test(legolas, gimli, alternative="two.sided") ##very tiny p value (smaller than 0.05 at 95% confidence) allows null hypothesis to be rejected, meaning there is substanial difference between heights of legolas and gimli actors
Welch Two Sample t-test
data: legolas and gimli
t = 25.408, df = 95.335, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
55.55795 64.97554
sample estimates:
mean of x mean of y
194.3599 134.0932 ##variance tests
var.test(legolas,gimli) ##p-value is very high (0.897) putting it way above 0.05, meaning we have failed to reject null hypothesis meanign there is no significant difference between variance in legolas and gimli
F test to compare two variances
data: legolas and gimli
F = 0.7135, num df = 49, denom df = 49, p-value = 0.2409
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.4048953 1.2573249
sample estimates:
ratio of variances
0.7135019 ##corelation tests for iris dataset
iris <- read.csv("iris.csv")
cor(iris$Sepal.Length[iris$Species == "setosa"],
iris$Sepal.Width[iris$Species == "setosa"]) ##being the correlation is close to 1, it shows sepal length and width are correlated for setosa[1] 0.7425467cor(iris$Sepal.Length[iris$Species == "versicolor"],
iris$Sepal.Width[iris$Species == "versicolor"]) ##being the correlation is between 0 and one, it shows that sepal length and width are correlated somtimes for versicolor[1] 0.5259107cor(iris$Sepal.Length[iris$Species == "virginica"],
iris$Sepal.Width[iris$Species == "virginica"]) ##being the correlation is closer to 0 than one, it shows that the sepal length and width are not strongly correlated for virginica[1] 0.4572278##chi-squared tests for deer dataset
deer <- read.csv("Deer.csv")
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)) ##as the p value is signficantly less then 0.05 it shows at 95% confidence there is a signficant difference in the number of deer caught per 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$Tb)) ##as the p value is significantly less than 0.05 it shows at 95% confidence there is a signficant difference in the number of deer with tb for farm, meaning it is not evenly distributedWarning in chisq.test(table(deer$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-15Quarto
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