graddegrees<-read.file("/home/emesekennedy/Data/Ch1/graddegrees.txt")
## Reading data with read.table()

View the name of the variables in the data:

names(graddegrees)
## [1] "Degree"        "PercentFemale"

Section 1.2 (continued)

Box plot of the chicken weight data grouped by the different diets:

bwplot(feed~weight,data=chickwts)

Create a data set with grades:

grades<-c(60, 65, 75, 80)

Look at the deviations from the mean:

grades-mean(grades)
## [1] -10  -5   5  10

Verify that the sum of the deviations from the mean is zero:

sum(grades-mean(grades))
## [1] 0

Find the standard deviation using the command sd() and the the favstats() command:

sd(grades)
## [1] 9.128709
favstats(grades)
##  min    Q1 median    Q3 max mean       sd n missing
##   60 63.75     70 76.25  80   70 9.128709 4       0

Transform the grades using a linear transformation:

newgrades<-1.1*grades+5

Look at the mean for of both the original and the new grades:

mean(grades)
## [1] 70
mean(newgrades)
## [1] 82

Verify that we can get the mean of the new grades by applying the same linear transformation to the mean of the original grades:

1.1*mean(grades)+5
## [1] 82

Save the statistics for both the original grades and the new grades:

stats<-favstats(grades)
newstats<-favstats(newgrades)

Compute the IQR for both the original grades and the new grades:

IQR<-stats[1,4]-stats[1,2]
IQR
## [1] 12.5
newIQR<-newstats[1,4]-newstats[1,2]
newIQR
## [1] 13.75

Verify that 1.1 times the IQR of the original grades gives the IQR of the new grades:

IQR*1.1
## [1] 13.75

Section 1.3

Load and create a histogram of the timetostart24 data with a density curve:

time24<-read.file("/home/emesekennedy/Data/Ch1/timetostart24.txt")
## Reading data with read.table()
histogram(~TimeToStart,data=time24,density=T)

Note: the vertical axis for the histogram must be densities (i.e. type=“density”). This is the default option, which is why we did not have to specify the type.

Load a new data set that is formatted a little differently than our previous data sets:

state<-read.file("/home/emesekennedy/Data/Ch1/collegebystate.txt",sep="\t",header=T)
## Reading data with read.table()

Create a histogram with a density curve showing the distribution of Undergraduate students in the USA by states:

histogram(~Undergrads,data=state,density=T)

As both the histogram and the density curve shows, the data is skewed to the right.

Graph a Normal curve centered at 3, with standard deviation 4, and shade the area under the curve until the value 2:

xpnorm(2,mean=3,sd=4)
## 
## If X ~ N(3,4), then 
## 
##  P(X <= 2) = P(Z <= -0.25) = 0.4013
##  P(X >  2) = P(Z >  -0.25) = 0.5987

## [1] 0.4012937

Graph a Normal curve centered at 3, with standard deviation 4, and shade 90% of the area under the curve:

xqnorm(0.9, mean=3,sd=4)
##  P(X <= 8.1262062621784) = 0.9
##  P(X >  8.1262062621784) = 0.1

## [1] 8.126206

Graph a Normal curve centered at 0, with standard deviation 5, and shade 10% of the area under the curve:

xqnorm(0.1, mean=0,sd=5)
##  P(X <= -6.407757827723) = 0.1
##  P(X >  -6.407757827723) = 0.9

## [1] -6.407758