## Lab Exercise 4 for R: Normal Distribution

### Exercise 1: Generate a sample from a normal distribution and throw the textbook at it

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 In R you can generate a variable filled with 100 random values from a normal distribution centered at mean 0 with a standard deviation of 1 > randNormVec <- rnorm(100,0,1) > randNormVec You can also utilize some functions in package 'pastecs' or 'psych' to obtain descriptive statistics. > install.packages("pastecs") > library(pastecs) > options(digits=4) > stat.desc(randNormVec) or ... > install.packages("psych") > library(psych) > describe(randNormVec) you can generate a frequency histogram of this generated sample either as a histogram, a density plot, or a cumulative distribution function ... > hist(randNormVec) > plot(density(randNormVec)) > plot(ecdf(randNormVec)) you can overlay the histogram with a normal distribution of the sample's mean and SD ...  and test for significant deviations from normality using Shapiro-Wilks > hist(randNormVec, breaks=20, prob=TRUE, xlab="generated", main="Normal overlay") > yBar <- mean(randNormVec) > ySD <- sqrt(var(randNormVec)) > curve(dnorm(x, mean=yBar, sd=ySD), col="red", lwd=2, add=TRUE) > shapiro.test(randNormVec) to save any of these generated graphs into an editable .svg document flank them with svg() ... and dev.off()  ... > svg("freqPlot.svg",width=8,height=5) > hist(randNormVec) > dev.off()