03-30-Hw-5

#load&see (use lecture note p.3)
library(datasets)

fL <- "http://www.amstat.org/publications/jse/datasets/sat.dat.txt"
dta <- read.table(fL, row.names=1)

names(dta) <- c("Spending", "PTR", "Salary", "PE", "Verbal", "Math", "SAT")
str(dta)

## 'data.frame':    50 obs. of  7 variables:
##  $ Spending: num  4.41 8.96 4.78 4.46 4.99 ...
##  $ PTR     : num  17.2 17.6 19.3 17.1 24 18.4 14.4 16.6 19.1 16.3 ...
##  $ Salary  : num  31.1 48 32.2 28.9 41.1 ...
##  $ PE      : int  8 47 27 6 45 29 81 68 48 65 ...
##  $ Verbal  : int  491 445 448 482 417 462 431 429 420 406 ...
##  $ Math    : int  538 489 496 523 485 518 477 468 469 448 ...
##  $ SAT     : int  1029 934 944 1005 902 980 908 897 889 854 ...

#devide the region
dta$Region <- state.division
with(dta, table(Region))

## Region
##        New England    Middle Atlantic     South Atlantic East South Central 
##                  6                  3                  8                  4 
## West South Central East North Central West North Central           Mountain 
##                  4                  5                  7                  8 
##            Pacific 
##                  5

#SAT by Salary
library(lattice)
xyplot(SAT ~ Salary, groups=Region, data=dta, type=c("g","r"), auto.key=list(columns=3))

#students’ SAT score was negatively associated with teachers’ salary in New England, East South Central, West South Central, West North Central and Mountain