# Script for 2010-08-25
#	Slight modifications were made	


# Create a sample of size 1e6
	x <- round(rnorm(1e6, mean=1000, sd=1000),0)


# Explore the mean-based functions
	mean(x)
	mean(x, trim=0.10)
	mean(x, trim=0.25)
	mean(x, trim=0.50)

	sd(x)
	var(x)


# Explore the median-based finctions
	quantile(x)
	median(x)
	range(x)

	IQR(x)




# Create a smaller dataset (n=10) to explain the geometric and harmonic means
	y <- runif(10)+1

# Recall: n is the sample size (N is the population size)
	n <- length(y)

# (Arithmetic) mean
	mean(y)

# Geometric mean
	geometricmean <- (prod(y))^(1/length(y))
	geometricmean

# Harmonic mean
	harmonicmean <- length(y) / sum(1/y)
	harmonicmean


#



# Graphing makes the data more understandable

# Stem-and-Leaf plot
	stem(x)

# Histogram
	hist(x)

# Boxplot
	boxplot(y)

# Add an outlier to the dataset
	v <- c(y,-1)

# Show a boxplot of this new data
	boxplot(v)

# Note that the outlier is signified by the circle