R Markdown

library(wooldridge)
library(rmarkdown)
data("cps78_85")
paged_table(cps78_85)
require(dplyr)
## Loading required package: dplyr
## 
## 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
data("cps78_85")
paged_table(cps78_85)
data<-cps78_85%>%
mutate(y78= ifelse(year==78,1,0))
cps78_85 %>%
group_by(year) %>%
summary(lm(lwage ~ y85*(educ + female) + exper + I(exper^2) + union + y78, data = cps78_85))
##       educ           south           nonwhite          female     
##  Min.   : 1.00   Min.   :0.0000   Min.   :0.0000   Min.   :0.000  
##  1st Qu.:12.00   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.000  
##  Median :12.00   Median :0.0000   Median :0.0000   Median :0.000  
##  Mean   :12.77   Mean   :0.2943   Mean   :0.1144   Mean   :0.417  
##  3rd Qu.:14.00   3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:1.000  
##  Max.   :18.00   Max.   :1.0000   Max.   :1.0000   Max.   :1.000  
##     married           exper          expersq           union       
##  Min.   :0.0000   Min.   : 0.00   Min.   :   0.0   Min.   :0.0000  
##  1st Qu.:0.0000   1st Qu.: 8.00   1st Qu.:  64.0   1st Qu.:0.0000  
##  Median :1.0000   Median :15.00   Median : 225.0   Median :0.0000  
##  Mean   :0.6541   Mean   :18.28   Mean   : 499.8   Mean   :0.2435  
##  3rd Qu.:1.0000   3rd Qu.:28.00   3rd Qu.: 784.0   3rd Qu.:0.0000  
##  Max.   :1.0000   Max.   :55.00   Max.   :3025.0   Max.   :1.0000  
##      lwage             age             year            y85        
##  Min.   :-0.470   Min.   :18.00   Min.   :78.00   Min.   :0.0000  
##  1st Qu.: 1.470   1st Qu.:27.00   1st Qu.:78.00   1st Qu.:0.0000  
##  Median : 1.833   Median :34.00   Median :78.00   Median :0.0000  
##  Mean   : 1.867   Mean   :36.54   Mean   :81.45   Mean   :0.4926  
##  3rd Qu.: 2.225   3rd Qu.:46.00   3rd Qu.:85.00   3rd Qu.:1.0000  
##  Max.   : 3.796   Max.   :64.00   Max.   :85.00   Max.   :1.0000  
##      y85fem         y85educ          y85union      
##  Min.   :0.000   Min.   : 0.000   Min.   :0.00000  
##  1st Qu.:0.000   1st Qu.: 0.000   1st Qu.:0.00000  
##  Median :0.000   Median : 0.000   Median :0.00000  
##  Mean   :0.226   Mean   : 6.413   Mean   :0.08856  
##  3rd Qu.:0.000   3rd Qu.:12.000   3rd Qu.:0.00000  
##  Max.   :1.000   Max.   :18.000   Max.   :1.00000
require(dplyr)
data("cps78_85")
paged_table(cps78_85)
cps78_85 %>%
  group_by(year) %>%
  summarise ("saatlik ücret (lwage)"= mean(lwage))
## # A tibble: 2 × 2
##    year `saatlik ücret (lwage)`
##   <int>                   <dbl>
## 1    78                    1.68
## 2    85                    2.06
cps78_85 %>%
  group_by(year) %>%
  summarise(n=n())
## # A tibble: 2 × 2
##    year     n
##   <int> <int>
## 1    78   550
## 2    85   534
summary(lm(lwage ~ y85*(educ + female) + exper + I(exper^2) + union , data = cps78_85))
## 
## Call:
## lm(formula = lwage ~ y85 * (educ + female) + exper + I(exper^2) + 
##     union, data = cps78_85)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.56098 -0.25828  0.00864  0.26571  2.11669 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.589e-01  9.345e-02   4.911 1.05e-06 ***
## y85          1.178e-01  1.238e-01   0.952   0.3415    
## educ         7.472e-02  6.676e-03  11.192  < 2e-16 ***
## female      -3.167e-01  3.662e-02  -8.648  < 2e-16 ***
## exper        2.958e-02  3.567e-03   8.293 3.27e-16 ***
## I(exper^2)  -3.994e-04  7.754e-05  -5.151 3.08e-07 ***
## union        2.021e-01  3.029e-02   6.672 4.03e-11 ***
## y85:educ     1.846e-02  9.354e-03   1.974   0.0487 *  
## y85:female   8.505e-02  5.131e-02   1.658   0.0977 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4127 on 1075 degrees of freedom
## Multiple R-squared:  0.4262, Adjusted R-squared:  0.4219 
## F-statistic:  99.8 on 8 and 1075 DF,  p-value: < 2.2e-16
summary(lm(lwage ~ y85*(educ + female) + exper + I(exper^2) + union -1, data = cps78_85))
## 
## Call:
## lm(formula = lwage ~ y85 * (educ + female) + exper + I(exper^2) + 
##     union - 1, data = cps78_85)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.56331 -0.24500  0.01702  0.27741  2.16580 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## y85         4.794e-01  1.006e-01   4.767 2.13e-06 ***
## educ        1.046e-01  2.767e-03  37.816  < 2e-16 ***
## female     -2.924e-01  3.667e-02  -7.973 3.92e-15 ***
## exper       3.355e-02  3.512e-03   9.552  < 2e-16 ***
## I(exper^2) -4.391e-04  7.794e-05  -5.634 2.24e-08 ***
## union       2.199e-01  3.040e-02   7.232 9.00e-13 ***
## y85:educ   -8.124e-03  7.710e-03  -1.054    0.292    
## y85:female  5.859e-02  5.157e-02   1.136    0.256    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4171 on 1076 degrees of freedom
## Multiple R-squared:  0.9543, Adjusted R-squared:  0.954 
## F-statistic:  2810 on 8 and 1076 DF,  p-value: < 2.2e-16