2020-03-30-Homework-5

顯示九個地區的名稱

library(datasets)
unique(datasets::state.division)

## [1] East South Central Pacific            Mountain           West South Central
## [5] New England        South Atlantic     East North Central West North Central
## [9] Middle Atlantic   
## 9 Levels: New England Middle Atlantic South Atlantic ... Pacific

載入SAT資料，重新命名

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 ...

擴充資料，將地名按九個地區分類

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

按不同地區，顯示教師薪資 & SAT成績的關係

dta.1 <- split(dta, dta$Region)
lapply((dta.1), function(x) coef(lm(x$SAT ~ x$Salary)))

## $`New England`
## (Intercept)    x$Salary 
## 913.8833194  -0.2067041 
## 
## $`Middle Atlantic`
## (Intercept)    x$Salary 
##  709.805961    3.911338 
## 
## $`South Atlantic`
## (Intercept)    x$Salary 
##  758.228238    3.755901 
## 
## $`East South Central`
## (Intercept)    x$Salary 
## 1106.412385   -2.621516 
## 
## $`West South Central`
## (Intercept)    x$Salary 
##  1795.56845   -28.19299 
## 
## $`East North Central`
## (Intercept)    x$Salary 
##   292.01099    18.43124 
## 
## $`West North Central`
## (Intercept)    x$Salary 
## 1114.182873   -1.317429 
## 
## $Mountain
## (Intercept)    x$Salary 
##  1419.66564   -13.79995 
## 
## $Pacific
## (Intercept)    x$Salary 
##  907.403873    0.355894

繪圖

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

New England, East South Central, West South Central, West North Central, Mountain五個地方，教師薪資和SAT成績是負面的關係