## Loading required package: carData閱讀資料
## [1] "year" "sex" "education" "vocabulary"## 'data.frame': 30351 obs. of 4 variables:
## $ year : num 1974 1974 1974 1974 1974 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 1 1 1 2 2 2 1 1 ...
## $ education : num 14 16 10 10 12 16 17 10 12 11 ...
## $ vocabulary: num 9 9 9 5 8 8 9 5 3 5 ...
## - attr(*, "na.action")= 'omit' Named int 1 2 3 4 5 6 7 8 9 10 ...
## ..- attr(*, "names")= chr "19720001" "19720002" "19720003" "19720004" ...## year sex education vocabulary
## Min. :1974 Female:17148 Min. : 0.00 Min. : 0.000
## 1st Qu.:1987 Male :13203 1st Qu.:12.00 1st Qu.: 5.000
## Median :1994 Median :12.00 Median : 6.000
## Mean :1995 Mean :13.03 Mean : 6.004
## 3rd Qu.:2006 3rd Qu.:15.00 3rd Qu.: 7.000
## Max. :2016 Max. :20.00 Max. :10.000## year sex education vocabulary
## 19740001 1974 Male 14 9
## 19740002 1974 Male 16 9
## 19740003 1974 Female 10 9
## 19740004 1974 Female 10 5
## 19740005 1974 Female 12 8
## 19740006 1974 Male 16 8男性資料
male <- subset(dta, dta$sex=="Male")
library(lattice)
xyplot(vocabulary ~ education, groups=year, data=male, type=c("g","r"), auto.key=list(columns=3))
## 根據資料1974至2016男性教育程度與字彙量關係逐年減少
## $`1974`
## (Intercept) x$education
## 1.5318434 0.3713183
##
## $`1976`
## (Intercept) x$education
## 1.6342960 0.3555403
##
## $`1978`
## (Intercept) x$education
## 0.9762161 0.3963762
##
## $`1982`
## (Intercept) x$education
## 0.9730291 0.3832637
##
## $`1984`
## (Intercept) x$education
## 1.678465 0.337124
##
## $`1987`
## (Intercept) x$education
## 0.8103651 0.3818373
##
## $`1988`
## (Intercept) x$education
## 1.0459936 0.3592442
##
## $`1989`
## (Intercept) x$education
## 1.0596176 0.3708525
##
## $`1990`
## (Intercept) x$education
## 1.7000935 0.3377029
##
## $`1991`
## (Intercept) x$education
## 1.2504604 0.3683962
##
## $`1993`
## (Intercept) x$education
## 1.6384884 0.3221049
##
## $`1994`
## (Intercept) x$education
## 1.8684770 0.3146151
##
## $`1996`
## (Intercept) x$education
## 0.8221711 0.3770325
##
## $`1998`
## (Intercept) x$education
## 1.5199973 0.3314754
##
## $`2000`
## (Intercept) x$education
## 1.1203888 0.3558918
##
## $`2004`
## (Intercept) x$education
## 1.4259424 0.3411153
##
## $`2006`
## (Intercept) x$education
## 2.1383454 0.2952926
##
## $`2008`
## (Intercept) x$education
## 1.4212286 0.3277987
##
## $`2010`
## (Intercept) x$education
## 1.7996389 0.3135749
##
## $`2012`
## (Intercept) x$education
## 1.7303105 0.3061534
##
## $`2014`
## (Intercept) x$education
## 1.4804789 0.3262112
##
## $`2016`
## (Intercept) x$education
## 1.8562367 0.3031146女性資料
Female <- subset(dta, dta$sex=="Female")
xyplot(vocabulary ~ education, groups=year, data=Female, type=c("g","r"), auto.key=list(columns=3))
## 根據資料1974至2016女性教育程度與字彙量關係逐年減少
Female.1 <- split(Female, Female$year)
lapply((Female.1), function(x) coef(lm(x$vocabulary ~ x$education)))## $`1974`
## (Intercept) x$education
## 1.5652579 0.3816095
##
## $`1976`
## (Intercept) x$education
## 1.7021281 0.3824002
##
## $`1978`
## (Intercept) x$education
## 1.3006416 0.4002707
##
## $`1982`
## (Intercept) x$education
## 0.9829602 0.3949758
##
## $`1984`
## (Intercept) x$education
## 1.4536872 0.3728698
##
## $`1987`
## (Intercept) x$education
## 0.9647931 0.3843508
##
## $`1988`
## (Intercept) x$education
## 1.1634561 0.3763999
##
## $`1989`
## (Intercept) x$education
## 1.0682600 0.3863606
##
## $`1990`
## (Intercept) x$education
## 0.4594812 0.4346902
##
## $`1991`
## (Intercept) x$education
## 1.1543766 0.3875821
##
## $`1993`
## (Intercept) x$education
## 1.7388287 0.3286325
##
## $`1994`
## (Intercept) x$education
## 1.6453365 0.3422146
##
## $`1996`
## (Intercept) x$education
## 1.1482811 0.3727178
##
## $`1998`
## (Intercept) x$education
## 1.4472751 0.3592843
##
## $`2000`
## (Intercept) x$education
## 1.9276040 0.3155532
##
## $`2004`
## (Intercept) x$education
## 2.104150 0.304056
##
## $`2006`
## (Intercept) x$education
## 2.7777171 0.2535376
##
## $`2008`
## (Intercept) x$education
## 2.6074315 0.2553971
##
## $`2010`
## (Intercept) x$education
## 1.3520300 0.3468821
##
## $`2012`
## (Intercept) x$education
## 1.7535298 0.3080832
##
## $`2014`
## (Intercept) x$education
## 2.3445239 0.2663464
##
## $`2016`
## (Intercept) x$education
## 2.0055919 0.2928955