library(wooldridge)
library(rmarkdown) # page_table komutuyla veri setini rmarkdown üzerinde gösterebilmek için
data("fertil1")
paged_table(fertil1)
require(dplyr)
## Zorunlu paket yükleniyor: dplyr
## Warning: package 'dplyr' was built under R version 4.2.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
require(dplyr)
cps78_85 %>%
group_by(year) %>%
summarise(n = n())
## # A tibble: 2 × 2
## year n
## <int> <int>
## 1 78 550
## 2 85 534
latif <-
summary(lm(lwage ~ y85*(educ + female) + exper + I(exper^2) + union, data = cps78_85))
(lm(lwage ~ y85*(educ + female) + -1 + exper + I(exper^2) + union, data = cps78_85))
##
## Call:
## lm(formula = lwage ~ y85 * (educ + female) + -1 + exper + I(exper^2) +
## union, data = cps78_85)
##
## Coefficients:
## y85 educ female exper I(exper^2) union
## 0.4793877 0.1046265 -0.2924136 0.0335464 -0.0004391 0.2198612
## y85:educ y85:female
## -0.0081243 0.0585871
summary(latif)
## Length Class Mode
## call 3 -none- call
## terms 3 terms call
## residuals 1084 -none- numeric
## coefficients 36 -none- numeric
## aliased 9 -none- logical
## sigma 1 -none- numeric
## df 3 -none- numeric
## r.squared 1 -none- numeric
## adj.r.squared 1 -none- numeric
## fstatistic 3 -none- numeric
## cov.unscaled 81 -none- numeric
latif <- fertil1 %>%
mutate(y78=-y74-y76-y80-y82-y84)%>%
mutate(y78=ifelse(year==72,1,0))
summary(latif)
## year educ meduc feduc
## Min. :72.00 Min. : 0.00 Min. : 0.000 Min. : 0.000
## 1st Qu.:74.00 1st Qu.:12.00 1st Qu.: 7.000 1st Qu.: 8.000
## Median :78.00 Median :12.00 Median : 8.000 Median :10.000
## Mean :78.14 Mean :12.69 Mean : 9.132 Mean : 9.716
## 3rd Qu.:82.00 3rd Qu.:14.00 3rd Qu.:12.000 3rd Qu.:12.000
## Max. :84.00 Max. :20.00 Max. :20.000 Max. :20.000
## age kids black east
## Min. :35.00 Min. :0.000 Min. :0.00000 Min. :0.0000
## 1st Qu.:38.00 1st Qu.:2.000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :43.00 Median :3.000 Median :0.00000 Median :0.0000
## Mean :43.48 Mean :2.743 Mean :0.08503 Mean :0.2489
## 3rd Qu.:48.00 3rd Qu.:4.000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :54.00 Max. :7.000 Max. :1.00000 Max. :1.0000
## northcen west farm othrural
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.3198 Mean :0.1081 Mean :0.1984 Mean :0.1019
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## town smcity y74 y76
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.3171 Mean :0.1258 Mean :0.1532 Mean :0.1346
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## y78 y80 y82 y84
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1382 Mean :0.1258 Mean :0.1647 Mean :0.1568
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## agesq y74educ y76educ y78educ
## Min. :1225 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:1444 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median :1849 Median : 0.000 Median : 0.000 Median : 0.000
## Mean :1925 Mean : 1.885 Mean : 1.647 Mean : 1.601
## 3rd Qu.:2304 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :2916 Max. :20.000 Max. :20.000 Max. :20.000
## y80educ y82educ y84educ
## Min. : 0.00 Min. : 0.000 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 0.00 Median : 0.000 Median : 0.00
## Mean : 1.62 Mean : 2.179 Mean : 2.08
## 3rd Qu.: 0.00 3rd Qu.: 0.000 3rd Qu.: 0.00
## Max. :20.00 Max. :20.000 Max. :20.00
latif <- cps78_85 %>%
mutate(sabitim = ifelse(married == 1 ,0 ,1))
summary(lm(latif))
##
## Call:
## lm(formula = latif)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0778 -0.0143 -0.0033 0.0077 3.9201
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.178e+00 4.216e-01 17.026 <2e-16 ***
## south -4.977e-03 8.312e-03 -0.599 0.5495
## nonwhite -6.889e-04 1.187e-02 -0.058 0.9537
## female 3.980e-03 1.126e-02 0.353 0.7239
## married -6.250e-03 8.408e-03 -0.743 0.4574
## exper -9.972e-01 2.353e-03 -423.803 <2e-16 ***
## expersq 4.442e-05 2.360e-05 1.882 0.0601 .
## union -3.296e-03 1.173e-02 -0.281 0.7787
## lwage 1.636e-02 9.002e-03 1.817 0.0695 .
## age 9.946e-01 2.071e-03 480.192 <2e-16 ***
## year -1.549e-01 5.115e-03 -30.281 <2e-16 ***
## y85 NA NA NA NA
## y85fem 1.663e-02 1.532e-02 1.086 0.2778
## y85educ 6.584e-03 2.751e-03 2.394 0.0169 *
## y85union -2.665e-03 1.791e-02 -0.149 0.8818
## sabitim NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1211 on 1070 degrees of freedom
## Multiple R-squared: 0.998, Adjusted R-squared: 0.998
## F-statistic: 4.153e+04 on 13 and 1070 DF, p-value: < 2.2e-16