Ellen Dingwall

# Spider running speed data
spider_speed <- c(1.25, 1.64, 1.91, 2.31, 2.37, 2.38, 2.84, 2.87, 2.93, 2.94, 2.98, 3.00, 3.09, 3.22, 3.41)

# Create a histogram
hist(spider_speed,
breaks = 10,
main = "Frequency Histogram of Spider Running Speed",
xlab = "Speed (cm/s)",
ylab = "Frequency",
col = "purple",
border = "black")

# Calculate sample mean
mean(spider_speed)

## [1] 2.609333

# Calculate sample standard deviation
sd(spider_speed)

## [1] 0.6178473

Mean: is well-centered in the data, no outlying points are skewing the mean. Standard Deviation is low: all data points are close to the mean

# Compute quartiles
quantile(spider_speed, probs = c(0.25, 0.5, 0.75))

##  25%  50%  75% 
## 2.34 2.87 2.99

Yes, for the most part, quartile calculations agree.

# Compute the IQR
IQR(spider_speed)

## [1] 0.65

Yes, IQR=Box Plot Length = .65

2a: Histogram B will have a larger mean, as there are more outlying data points on the right side of the histogram, and this will skew the mean to the right, making the mean larger. 2b: Histogram B will also have a larger SD, as the range of values is higher, meaning that the data is more spread out from the mean, and would have a higher SD value. 2c: adding a rug to the histograms would have helped to demonstrate where points fell. Other helpfuls things could include adding a mean, standard deviation, and/or boxplot.