ex 01

library(MASS)
ls("package:MASS")[94:103]
##  [1] "minn38"            "motors"            "muscle"           
##  [4] "mvrnorm"           "nclass.freq"       "neg.bin"          
##  [7] "negative.binomial" "negexp.SSival"     "newcomb"          
## [10] "nlschools"

ex 02

dta<-women
dta$height_centered <-with(dta,(height-mean(height)))
coef(lm(weight ~ height_centered, data = dta))
##     (Intercept) height_centered 
##        136.7333          3.4500

平移不會改變斜率

ex 03

c(women)
## $height
##  [1] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 
## $weight
##  [1] 115 117 120 123 126 129 132 135 139 142 146 150 154 159 164
c(as.matrix(women))
##  [1]  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72 115 117
## [18] 120 123 126 129 132 135 139 142 146 150 154 159 164

c(women)將women裡面的身高體重用data frame的形式合併， 而c(as.matrix(women))的資料呈現是以矩陣的方式。

ex 04

head(birthwt)
##    low age lwt race smoke ptl ht ui ftv  bwt
## 85   0  19 182    2     0   0  0  1   0 2523
## 86   0  33 155    3     0   0  0  0   3 2551
## 87   0  20 105    1     1   0  0  0   1 2557
## 88   0  21 108    1     1   0  0  1   2 2594
## 89   0  18 107    1     1   0  0  1   0 2600
## 91   0  21 124    3     0   0  0  0   0 2622
c("White", "Black", "Other")[birthwt$race]
##   [1] "Black" "Other" "White" "White" "White" "Other" "White" "Other"
##   [9] "White" "White" "Other" "Other" "Other" "Other" "White" "White"
##  [17] "Black" "White" "Other" "White" "Other" "White" "White" "Other"
##  [25] "Other" "White" "White" "White" "Black" "Black" "Black" "White"
##  [33] "Black" "White" "Black" "White" "White" "White" "White" "White"
##  [41] "Black" "White" "Black" "White" "White" "White" "White" "Other"
##  [49] "White" "Other" "White" "Other" "White" "White" "Other" "Other"
##  [57] "Other" "Other" "Other" "Other" "Other" "Other" "Other" "White"
##  [65] "Other" "Other" "Other" "Other" "White" "Black" "White" "Other"
##  [73] "Other" "Black" "White" "Black" "White" "White" "Black" "White"
##  [81] "White" "White" "Other" "Other" "Other" "Other" "Other" "White"
##  [89] "White" "White" "White" "Other" "White" "White" "White" "White"
##  [97] "White" "White" "White" "White" "White" "White" "Other" "White"
## [105] "Other" "Black" "White" "White" "White" "Black" "White" "Other"
## [113] "White" "White" "White" "Other" "White" "Other" "White" "Other"
## [121] "White" "Other" "White" "White" "White" "White" "White" "White"
## [129] "White" "White" "Other" "White" "Black" "Other" "Other" "Other"
## [137] "Other" "Black" "Other" "White" "White" "White" "Other" "Other"
## [145] "White" "White" "Black" "White" "Other" "Other" "Other" "White"
## [153] "White" "White" "White" "Other" "Black" "White" "Black" "Other"
## [161] "White" "Other" "Other" "Other" "Black" "White" "Other" "Other"
## [169] "White" "White" "Black" "Black" "Black" "Other" "Other" "White"
## [177] "White" "White" "White" "Black" "Other" "Other" "White" "Other"
## [185] "White" "Other" "Other" "Black" "White"

第一筆資料能看見所有的數據，而種族是其中一個數據， 經過處理後，第二筆資料是直接提取種族的資料，並且以文字呈現所有樣本的種族屬性。