Trout.250 <- c(508, 479, 545, 531, 559, 422, 547, 525, 420, 491, 508, 511, 569, 453,
533, 460, 523, 540, 463, 502)
Trout.300 <- c(461, 464, 344, 559, 445, 617, 402, 531, 535, 413, 456, 479, 393, 504,
416, 468, 368, 519, 523, 531)
Farmed.Trout <- data.frame(cbind(Trout.250, Trout.300)) # combine as data frame
str(Farmed.Trout) # wide data - weights of 2 groups in separate columns
## 'data.frame': 20 obs. of 2 variables:
## $ Trout.250: num 508 479 545 531 559 422 547 525 420 491 ...
## $ Trout.300: num 461 464 344 559 445 617 402 531 535 413 ...
#> 'data.frame': 20 obs. of 2 variables:
#> $ Trout.250: num 508 479 545 531 559 422 547 525 420 491 ...
#> $ Trout.300: num 461 464 344 559 445 617 402 531 535 413 ...
summary(Farmed.Trout) # compare values of 2 groups
## Trout.250 Trout.300
## Min. :420.0 Min. :344.0
## 1st Qu.:475.0 1st Qu.:415.2
## Median :509.5 Median :466.0
## Mean :504.4 Mean :471.4
## 3rd Qu.:534.8 3rd Qu.:525.0
## Max. :569.0 Max. :617.0
#> Trout.250 Trout.300
#> Min. :420 Min. :344
#> 1st Qu.:475 1st Qu.:415
#> Median :510 Median :466
#> Mean :504 Mean :471
#> 3rd Qu.:535 3rd Qu.:525
#> Max. :569 Max. :617
# wide data format: use , not ~
with(Farmed.Trout,boxplot(Trout.250,Trout.300,
col= "pink",
main= "Weights of Aquaculture Raised Rainbow Trout",
xlab= "Pen density (No. fish)",
ylab= "Weight (g)",
ylim= c(300,650),
names= c("250 per pen","300 per pen"), # group names
las= 1,
boxwex =0.6))
Figure 11.2: Box plot of rainbow trout weight distributions from two
aquaculture pen densities (250 fish/pen and 300 fish/pen)
with(Farmed.Trout, var.test(Trout.250, Trout.300))
##
## F test to compare two variances
##
## data: Trout.250 and Trout.300
## F = 0.38583, num df = 19, denom df = 19, p-value = 0.04421
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.1527172 0.9747866
## sample estimates:
## ratio of variances
## 0.3858324
#>
#> F test to compare two variances
#>
#> data: Trout.250 and Trout.300
#> F = 0.4, num df = 19, denom df = 19, p-value = 0.04
#> alternative hypothesis: true ratio of variances is not equal to 1
#> 95 percent confidence interval:
#> 0.153 0.975
#> sample estimates:
#> ratio of variances
#> 0.386